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Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities (2405.00711v2)

Published 25 Apr 2024 in cs.CL, cs.AI, and cs.CY

Abstract: In recent years, generative artificial intelligence models, represented by LLMs and Diffusion Models (DMs), have revolutionized content production methods. These artificial intelligence-generated content (AIGC) have become deeply embedded in various aspects of daily life and work. However, these technologies have also led to the emergence of Fake Artificial Intelligence Generated Content (FAIGC), posing new challenges in distinguishing genuine information. It is crucial to recognize that AIGC technology is akin to a double-edged sword; its potent generative capabilities, while beneficial, also pose risks for the creation and dissemination of FAIGC. In this survey, We propose a new taxonomy that provides a more comprehensive breakdown of the space of FAIGC methods today. Next, we explore the modalities and generative technologies of FAIGC. We introduce FAIGC detection methods and summarize the related benchmark from various perspectives. Finally, we discuss outstanding challenges and promising areas for future research.

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References (311)
  1. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 .
  2. Efficient fake news detection mechanism using enhanced deep learning model. Applied Sciences 12, 1743.
  3. Machine learning techniques for spam detection in email and iot platforms: analysis and research challenges. Security and Communication Networks 2022, 1–19.
  4. Characterizing attribution and fluency tradeoffs for retrieval-augmented large language models. arXiv:2302.05578.
  5. Hyperstyle: Stylegan inversion with hypernetworks for real image editing, in: Proceedings of the IEEE/CVF conference on computer Vision and pattern recognition, pp. 18511--18521.
  6. A review of modern audio deepfake detection methods: Challenges and future directions. Algorithms 15, 155.
  7. Anthropic, 2024. Introducing the next generation of claude. https://www.anthropic.com/news/claude-3-family .
  8. A review of techniques to detect the gan-generated fake images. Generative Adversarial Networks for Image-to-Image Translation , 125--159.
  9. Exploring deep neural networks for rumor detection. Journal of Ambient Intelligence and Humanized Computing 12, 4315--4333.
  10. The internal state of an llm knows when it’s lying. arXiv:2304.13734.
  11. Voice conversion with just nearest neighbors. arXiv:2305.18975.
  12. The impact of user--generated content (ugc) on product reviews towards online purchasing--a conceptual framework. Procedia Economics and Finance 37, 337--342.
  13. Mitigating open-vocabulary caption hallucinations. arXiv:2312.03631.
  14. Rumor detection on social media with bi-directional graph convolutional networks. Proceedings of the AAAI Conference on Artificial Intelligence 34, 549--556. URL: https://ojs.aaai.org/index.php/AAAI/article/view/5393, doi:10.1609/aaai.v34i01.5393.
  15. A survey on fake news and rumour detection techniques. Information sciences 497, 38--55.
  16. Large scale gan training for high fidelity natural image synthesis. arXiv:1809.11096.
  17. Language models are few-shot learners. Advances in neural information processing systems 33, 1877--1901.
  18. User-generated content (ugc) in tourism: Benefits and concerns of online consumers., in: ECIS, Citeseer. pp. 417--429.
  19. Glitch in the matrix: A large scale benchmark for content driven audio--visual forgery detection and localization. Computer Vision and Image Understanding 236, 103818.
  20. Hallucinated but factual! inspecting the factuality of hallucinations in abstractive summarization, in: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland. pp. 3340--3354. URL: https://aclanthology.org/2022.acl-long.236, doi:10.18653/v1/2022.acl-long.236.
  21. Autohall: Automated hallucination dataset generation for large language models. arXiv:2310.00259.
  22. End-to-end object detection with transformers, in: European conference on computer vision, Springer. pp. 213--229.
  23. Pix2video: Video editing using image diffusion, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 23206--23217.
  24. Deepfake: an overview, in: Proceedings of Second International Conference on Computing, Communications, and Cyber-Security: IC4S 2020, Springer. pp. 557--566.
  25. Stablevideo: Text-driven consistency-aware diffusion video editing, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 23040--23050.
  26. Graph-based modeling of online communities for fake news detection. arXiv preprint arXiv:2008.06274 .
  27. Jailbreaking black box large language models in twenty queries. arXiv:2310.08419.
  28. Can llm-generated misinformation be detected? arXiv preprint arXiv:2309.13788 .
  29. Combating misinformation in the age of llms: Opportunities and challenges. arXiv preprint arXiv:2311.05656 .
  30. Finance worker pays out 25 million after video call with deepfake ‘chief financial officer’. CNN URL: https://edition.cnn.com/2024/02/04/asia/deepfake-cfo-scam-hong-kong-intl-hnk/index.html.
  31. Alpagasus: Training a better alpaca with fewer data. arXiv preprint arXiv:2307.08701 .
  32. Self-supervised learning of adversarial example: Towards good generalizations for deepfake detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 18710--18719.
  33. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection, in: Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers 22, Springer. pp. 40--52.
  34. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors. arXiv:2308.10848.
  35. Unveiling the siren’s song: Towards reliable fact-conflicting hallucination detection. arXiv preprint arXiv:2310.12086 .
  36. Unified hallucination detection for multimodal large language models. arXiv preprint arXiv:2402.03190 .
  37. Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality. Scientific reports 10, 5887.
  38. Mitigating hallucination in visual language models with visual supervision. arXiv preprint arXiv:2311.16479 .
  39. Ugc video sharing: Measurement and analysis. Intelligent Multimedia Communication: Techniques and Applications , 367--402.
  40. Consumers’ reliance on product information and recommendations found in ugc. Journal of interactive advertising 8, 38--49.
  41. Factool: Factuality detection in generative ai -- a tool augmented framework for multi-task and multi-domain scenarios. arXiv:2307.13528.
  42. Rumor propagation is amplified by echo chambers in social media. Scientific reports 10, 310.
  43. KCTS: Knowledge-constrained tree search decoding with token-level hallucination detection, in: Bouamor, H., Pino, J., Bali, K. (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Singapore. pp. 14035--14053. URL: https://aclanthology.org/2023.emnlp-main.867, doi:10.18653/v1/2023.emnlp-main.867.
  44. Dola: Decoding by contrasting layers improves factuality in large language models. arXiv preprint arXiv:2309.03883 .
  45. Combining efficientnet and vision transformers for video deepfake detection, in: International conference on image analysis and processing, Springer. pp. 219--229.
  46. Audio-visual person-of-interest deepfake detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 943--952.
  47. Survey of review spam detection using machine learning techniques. Journal of Big Data 2, 1--24.
  48. Holistic analysis of hallucination in gpt-4v(ision): Bias and interference challenges. arXiv:2311.03287.
  49. Detecting and mitigating hallucinations in machine translation: Model internal workings alone do well, sentence similarity even better. arXiv:2212.08597.
  50. Exploring consumer motivations for creating user-generated content. Journal of interactive advertising 8, 16--25.
  51. Controllable video generation through global and local motion dynamics, in: European Conference on Computer Vision, Springer. pp. 68--84.
  52. Masterkey: Automated jailbreaking of large language model chatbots, in: Proc. ISOC NDSS.
  53. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 .
  54. Chain-of-verification reduces hallucination in large language models. arXiv preprint arXiv:2309.11495 .
  55. Review of audio deepfake detection techniques: Issues and prospects. Expert Systems 40, e13322.
  56. Implicit identity leakage: The stumbling block to improving deepfake detection generalization, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3994--4004.
  57. How robust is google’s bard to adversarial image attacks? arXiv preprint arXiv:2309.11751 .
  58. User preference-aware fake news detection, in: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp. 2051--2055.
  59. Neural path hunter: Reducing hallucination in dialogue systems via path grounding, in: Moens, M.F., Huang, X., Specia, L., Yih, S.W.t. (Eds.), Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. pp. 2197--2214. URL: https://aclanthology.org/2021.emnlp-main.168, doi:10.18653/v1/2021.emnlp-main.168.
  60. Halo: Estimation and reduction of hallucinations in open-source weak large language models. arXiv preprint arXiv:2308.11764 .
  61. Retrieval-generation synergy augmented large language models. arXiv preprint arXiv:2310.05149 .
  62. Towards opening the black box of neural machine translation: Source and target interpretations of the transformer, in: Goldberg, Y., Kozareva, Z., Zhang, Y. (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. pp. 8756--8769. URL: https://aclanthology.org/2022.emnlp-main.599, doi:10.18653/v1/2022.emnlp-main.599.
  63. Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computational Linguistics 8, 539--555. URL: https://aclanthology.org/2020.tacl-1.35, doi:10.1162/tacl_a_00330.
  64. Wavefake: A data set to facilitate audio deepfake detection. arXiv preprint arXiv:2111.02813 .
  65. Rumor cascades, in: proceedings of the international AAAI conference on web and social media, pp. 101--110.
  66. InfoSurgeon: Cross-media fine-grained information consistency checking for fake news detection, in: Zong, C., Xia, F., Li, W., Navigli, R. (Eds.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online. pp. 1683--1698. URL: https://aclanthology.org/2021.acl-long.133, doi:10.18653/v1/2021.acl-long.133.
  67. RARR: Researching and revising what language models say, using language models, in: Rogers, A., Boyd-Graber, J., Okazaki, N. (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Toronto, Canada. pp. 16477--16508. URL: https://aclanthology.org/2023.acl-long.910, doi:10.18653/v1/2023.acl-long.910.
  68. Generalized spoofing detection inspired from audio generation artifacts. arXiv preprint arXiv:2104.04111 .
  69. Aigcs confuse ai too: Investigating and explaining synthetic image-induced hallucinations in large vision-language models. arXiv:2403.08542.
  70. Fake news: A definition. Informal logic 38, 84--117.
  71. Text-to-audio generation using instruction-tuned llm and latent diffusion model. arXiv preprint arXiv:2304.13731 .
  72. ‘fake news’ is the invention of a liar: How false information circulates within the hybrid news system. Current sociology 67, 625--642.
  73. Generative language models and automated influence operations: Emerging threats and potential mitigations. arXiv:2301.04246.
  74. Figstep: Jailbreaking large vision-language models via typographic visual prompts. arXiv preprint arXiv:2311.05608 .
  75. Trustworthy or shady? exploring the influence of verifying and visualizing user-generated content (ugc) on online journalism’s trustworthiness. Journalism Studies 20, 500--522.
  76. Detecting and preventing hallucinations in large vision language models. arXiv preprint arXiv:2308.06394 .
  77. Prompttts: Controllable text-to-speech with text descriptions, in: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. pp. 1--5.
  78. Mixed graph neural network-based fake news detection for sustainable vehicular social networks. IEEE Transactions on Intelligent Transportation Systems .
  79. Lm-switch: Lightweight language model conditioning in word embedding space. arXiv preprint arXiv:2305.12798 .
  80. Flexible diffusion modeling of long videos. Advances in Neural Information Processing Systems 35, 27953--27965.
  81. Denoising diffusion probabilistic models. Advances in neural information processing systems 33, 6840--6851.
  82. German magazine editor is fired over a.i. michael schumacher interview. The New York Times URL: https://www.nytimes.com/2023/04/24/business/media/michael-schumacher-ai-fake-interview.html.
  83. Implicit identity driven deepfake face swapping detection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4490--4499.
  84. Layered controllable video generation, in: European Conference on Computer Vision, Springer. pp. 546--564.
  85. Deepfake mnist+: a deepfake facial animation dataset, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1973--1982.
  86. Composer: Creative and controllable image synthesis with composable conditions. arXiv:2302.09778.
  87. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv:2311.05232.
  88. Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation. arXiv:2311.17911.
  89. Diffusion model-based image editing: A survey. arXiv:2402.17525.
  90. Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity. arXiv preprint arXiv:2403.14403 .
  91. Frepgan: Robust deepfake detection using frequency-level perturbations, in: Proceedings of the AAAI conference on artificial intelligence, Association for the Advancement of Artificial Intelligence, online. pp. 1060--1068.
  92. Mistral 7b. arXiv preprint arXiv:2310.06825 .
  93. Disinformation detection: An evolving challenge in the age of llms. arXiv:2309.15847.
  94. Hallucination augmented contrastive learning for multimodal large language model. arXiv:2312.06968.
  95. Artprompt: Ascii art-based jailbreak attacks against aligned llms. arXiv preprint arXiv:2402.11753 .
  96. Similarity-aware multimodal prompt learning for fake news detection. Information Sciences 647, 119446.
  97. Active retrieval augmented generation, in: Bouamor, H., Pino, J., Bali, K. (Eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Singapore. pp. 7969--7992. URL: https://aclanthology.org/2023.emnlp-main.495, doi:10.18653/v1/2023.emnlp-main.495.
  98. Multimodal fusion with recurrent neural networks for rumor detection on microblogs, in: Proceedings of the 25th ACM international conference on Multimedia, pp. 795--816.
  99. Automatically auditing large language models via discrete optimization, in: International Conference on Machine Learning, PMLR. pp. 15307--15329.
  100. Fake news, in: Oxford Research Encyclopedia of Communication.
  101. Exploiting programmatic behavior of llms: Dual-use through standard security attacks. arXiv preprint arXiv:2302.05733 .
  102. Knowledge graph-augmented language models for knowledge-grounded dialogue generation. arXiv preprint arXiv:2305.18846 .
  103. Improved deepfake detection using whisper features. arXiv preprint arXiv:2306.01428 .
  104. Fakeavceleb: A novel audio-video multimodal deepfake dataset. arXiv preprint arXiv:2108.05080 .
  105. Visual user-generated content verification in journalism: An overview. IEEE Access 11, 6748--6769.
  106. Audio deepfakes: A survey. Frontiers in Big Data 5, 1001063.
  107. Speak, read and prompt: High-fidelity text-to-speech with minimal supervision. Transactions of the Association for Computational Linguistics 11, 1703--1718.
  108. Diffusionclip: Text-guided diffusion models for robust image manipulation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2426--2435.
  109. Guided-tts 2: A diffusion model for high-quality adaptive text-to-speech with untranscribed data. arXiv preprint arXiv:2205.15370 .
  110. Controllable video generation with text-based instructions. IEEE transactions on multimedia .
  111. Mfaan: Unveiling audio deepfakes with a multi-feature authenticity network, in: 2023 9th International Conference on Signal Processing and Communication (ICSC), IEEE. pp. 585--590.
  112. User-generated content. IEEE Pervasive Computing 7, 10--11.
  113. Email spam detection using machine learning algorithms, in: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), IEEE. pp. 108--113.
  114. False information on web and social media: A survey. arXiv preprint arXiv:1804.08559 .
  115. Disinformation on the web: Impact, characteristics, and detection of wikipedia hoaxes, in: Proceedings of the 25th international conference on World Wide Web, pp. 591--602.
  116. Deepfake: a social construction of technology perspective. Current Issues in Tourism 24, 1798--1802.
  117. Diffusion models already have a semantic latent space. arXiv preprint arXiv:2210.10960 .
  118. The science of fake news. Science 359, 1094--1096.
  119. Platypus: Quick, cheap, and powerful refinement of llms. arXiv preprint arXiv:2308.07317 .
  120. Something that they never said: multimodal disinformation and source vividness in understanding the power of ai-enabled deepfake news. Media Psychology 25, 531--546.
  121. Factuality enhanced language models for open-ended text generation. Advances in Neural Information Processing Systems 35, 34586--34599.
  122. Shape-aware text-driven layered video editing, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14317--14326.
  123. Mitigating object hallucinations in large vision-language models through visual contrastive decoding. arXiv preprint arXiv:2311.16922 .
  124. A continual deepfake detection benchmark: Dataset, methods, and essentials, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1339--1349.
  125. Large language models with controllable working memory, in: Rogers, A., Boyd-Graber, J., Okazaki, N. (Eds.), Findings of the Association for Computational Linguistics: ACL 2023, Association for Computational Linguistics, Toronto, Canada. pp. 1774--1793. URL: https://aclanthology.org/2023.findings-acl.112, doi:10.18653/v1/2023.findings-acl.112.
  126. Halueval: A large-scale hallucination evaluation benchmark for large language models. URL: https://arxiv.org/abs/2305.11747.
  127. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv:2301.12597.
  128. Freevc: Towards high-quality text-free one-shot voice conversion, in: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. pp. 1--5.
  129. Inference-time intervention: Eliciting truthful answers from a language model. Advances in Neural Information Processing Systems 36.
  130. Textbooks are all you need ii: phi-1.5 technical report. arXiv preprint arXiv:2309.05463 .
  131. Evaluating object hallucination in large vision-language models. arXiv preprint arXiv:2305.10355 .
  132. Gligen: Open-set grounded text-to-image generation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22511--22521.
  133. Celeb-df: A large-scale challenging dataset for deepfake forensics, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3207--3216.
  134. Styletts-vc: One-shot voice conversion by knowledge transfer from style-based tts models, in: 2022 IEEE Spoken Language Technology Workshop (SLT), IEEE. pp. 920--927.
  135. Uhgeval: Benchmarking the hallucination of chinese large language models via unconstrained generation. arXiv preprint arXiv:2311.15296 .
  136. Zero-shot rumor detection with propagation structure via prompt learning, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 5213--5221.
  137. Detecting multimedia generated by large ai models: A survey. arXiv:2402.00045.
  138. Mitigating hallucination in large multi-modal models via robust instruction tuning, in: The Twelfth International Conference on Learning Representations.
  139. Mitigating hallucination in large multi-modal models via robust instruction tuning. arXiv:2306.14565.
  140. Improved baselines with visual instruction tuning. arXiv:2310.03744.
  141. Visual instruction tuning. arXiv:2304.08485.
  142. Visual instruction tuning. Advances in neural information processing systems 36.
  143. A survey on hallucination in large vision-language models. arXiv preprint arXiv:2402.00253 .
  144. Video-p2p: Video editing with cross-attention control. arXiv preprint arXiv:2303.04761 .
  145. A token-level reference-free hallucination detection benchmark for free-form text generation, in: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Dublin, Ireland. pp. 6723--6737. URL: https://aclanthology.org/2022.acl-long.464, doi:10.18653/v1/2022.acl-long.464.
  146. Autodan: Generating stealthy jailbreak prompts on aligned large language models. arXiv preprint arXiv:2310.04451 .
  147. Bolaa: Benchmarking and orchestrating llm-augmented autonomous agents. arXiv:2308.05960.
  148. Generation and detection of manipulated multimodal audiovisual content: Advances, trends and open challenges. Information Fusion 103, 102103.
  149. Seeing is not always believing: Benchmarking human and model perception of ai-generated images. Advances in Neural Information Processing Systems 36.
  150. Zero-resource hallucination prevention for large language models. arXiv preprint arXiv:2309.02654 .
  151. Rumor detection on twitter with tree-structured recursive neural networks, Association for Computational Linguistics.
  152. A comparative analysis of graph neural networks and commonly used machine learning algorithms on fake news detection, in: 2022 7th international conference on data science and machine learning applications (CDMA), IEEE. pp. 97--102.
  153. An efficient spam detection technique for iot devices using machine learning. IEEE Transactions on Industrial Informatics 17, 903--912.
  154. Deepfake detection for human face images and videos: A survey. Ieee Access 10, 18757--18775.
  155. Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models. arXiv preprint arXiv:2303.08896 .
  156. Legal protection of revenge and deepfake porn victims in the european union: Findings from a comparative legal study. Trauma, Violence, & Abuse 25, 117--129.
  157. Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities. Expert Systems with Applications 153, 112986.
  158. Addressing the harms of ai-generated inauthentic content. Nature Machine Intelligence 5, 679--680.
  159. Microsoft, 2023. Copliot. https://www.bing.com/copilot .
  160. Fine-grained hallucination detection and editing for language models. arXiv preprint arXiv:2401.06855 .
  161. Detectgpt: Zero-shot machine-generated text detection using probability curvature, in: International Conference on Machine Learning, PMLR. pp. 24950--24962.
  162. Video manipulations beyond faces: A dataset with human-machine analysis, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 643--652.
  163. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453 .
  164. Use of llms for illicit purposes: Threats, prevention measures, and vulnerabilities. arXiv preprint arXiv:2308.12833 .
  165. A comprehensive review on fake news detection with deep learning. IEEE access 9, 156151--156170.
  166. Does audio deepfake detection generalize? arXiv preprint arXiv:2203.16263 .
  167. Self-contradictory hallucinations of large language models: Evaluation, detection and mitigation. arXiv preprint arXiv:2305.15852 .
  168. Studies of user-generated content: A systematic review. Journalism 18, 1256--1273.
  169. Df-platter: multi-face heterogeneous deepfake dataset, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9739--9748.
  170. Nationality bias in text generation, in: Vlachos, A., Augenstein, I. (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Dubrovnik, Croatia. pp. 116--122. URL: https://aclanthology.org/2023.eacl-main.9, doi:10.18653/v1/2023.eacl-main.9.
  171. Rsgan: face swapping and editing using face and hair representation in latent spaces. arXiv preprint arXiv:1804.03447 .
  172. Fsgan: Subject agnostic face swapping and reenactment, in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 7184--7193.
  173. Jailbreaking attack against multimodal large language model. arXiv preprint arXiv:2402.02309 .
  174. Entity cloze by date: What LMs know about unseen entities, in: Carpuat, M., de Marneffe, M.C., Meza Ruiz, I.V. (Eds.), Findings of the Association for Computational Linguistics: NAACL 2022, Association for Computational Linguistics, Seattle, United States. pp. 693--702. URL: https://aclanthology.org/2022.findings-naacl.52, doi:10.18653/v1/2022.findings-naacl.52.
  175. OpenAI, 2019. Webtext-style gpt2-generated dataset. https://github.com/openai/gpt-2-output-dataset.
  176. OpenAI, 2022. Introducing chatgpt. https://openai.com/blog/chatgpt .
  177. OpenAI, 2023. Gpt-4v(ision) system card. https://openai.com/research/gpt-4v-system-card .
  178. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35, 27730--27744.
  179. On the risk of misinformation pollution with large language models. arXiv:2305.13661.
  180. Deep multimodal learning for affective analysis and retrieval. IEEE Transactions on Multimedia 17, 2008--2020.
  181. Data and its (dis) contents: A survey of dataset development and use in machine learning research. Patterns 2.
  182. The refinedweb dataset for falcon llm: outperforming curated corpora with web data, and web data only. arXiv preprint arXiv:2306.01116 .
  183. The psychology of fake news. Trends in cognitive sciences 25, 388--402.
  184. Discovering language model behaviors with model-written evaluations, in: Rogers, A., Boyd-Graber, J., Okazaki, N. (Eds.), Findings of the Association for Computational Linguistics: ACL 2023, Association for Computational Linguistics, Toronto, Canada. pp. 13387--13434. URL: https://aclanthology.org/2023.findings-acl.847, doi:10.18653/v1/2023.findings-acl.847.
  185. Fake news detection: A survey of graph neural network methods. Applied Soft Computing , 110235.
  186. Measuring and narrowing the compositionality gap in language models. arXiv preprint arXiv:2210.03350 .
  187. Fatezero: Fusing attentions for zero-shot text-based video editing, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 15932--15942.
  188. Improving language understanding by generative pre-training .
  189. Question decomposition improves the faithfulness of model-generated reasoning. arXiv preprint arXiv:2307.11768 .
  190. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems 36.
  191. In-context retrieval-augmented language models. Transactions of the Association for Computational Linguistics 11, 1316--1331.
  192. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1, 3.
  193. Deepfake detection: A systematic literature review. IEEE access 10, 25494--25513.
  194. Rumor, misinformation among web: a contemporary review of rumor detection techniques during different web waves. Concurrency and Computation: Practice and Experience 34, e6479.
  195. Reading between the lines: untwining online user-generated content using sentiment analysis. Journal of Research in Interactive Marketing 15, 401--418.
  196. A survey of hallucination in large foundation models. arXiv preprint arXiv:2309.05922 .
  197. reddit, . Dan is my new friend. URL: {https://www.reddit.com/r/ChatGPT/comments/zlcyr9/comment/j05xuiw/}.
  198. For: A dataset for synthetic speech detection, in: 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD), IEEE. pp. 1--10.
  199. Investigating the factual knowledge boundary of large language models with retrieval augmentation. arXiv preprint arXiv:2307.11019 .
  200. ‘fake news’: Incorrect, but hard to correct. the role of cognitive ability on the impact of false information on social impressions. Intelligence 65, 107--110.
  201. High-resolution image synthesis with latent diffusion models, in: CVPR.
  202. A deep ensemble framework for fake news detection and classification. arXiv preprint arXiv:1811.04670 .
  203. Delucionqa: Detecting hallucinations in domain-specific question answering. arXiv preprint arXiv:2312.05200 .
  204. The “so-called” ugc: an updated definition of user-generated content in the age of social media. Online Information Review 46, 95--113.
  205. Defining the types of" fakers" in social media .
  206. Sentiment analysis: Extracting decision-relevant knowledge from ugc, in: Information and Communication Technologies in Tourism 2014: Proceedings of the International Conference in Dublin, Ireland, January 21-24, 2014, Springer. pp. 253--265.
  207. Reinforcement learning from human feedback: Progress and challenges. YouTube URL: https://www.youtube.com/watch?v=hhiLw5Q_UFg.
  208. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 .
  209. Discovery and classification of the underlying emotions in the user generated content (ugc), in: Information and Communication Technologies in Tourism 2016: Proceedings of the International Conference in Bilbao, Spain, February 2-5, 2016, Springer. pp. 225--237.
  210. Multimodal analysis of user-generated multimedia content. Springer.
  211. Multimodal analysis of user-generated content in support of social media applications, in: Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval, pp. 423--426.
  212. Towards understanding sycophancy in language models. arXiv preprint arXiv:2310.13548 .
  213. “why is this misleading?”: Detecting news headline hallucinations with explanations, in: Proceedings of the ACM Web Conference 2023, pp. 1662--1672.
  214. Trusting your evidence: Hallucinate less with context-aware decoding. arXiv preprint arXiv:2305.14739 .
  215. defend: Explainable fake news detection, in: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 395--405.
  216. Using gans to synthesise minimum training data for deepfake generation. arXiv preprint arXiv:2011.05421 .
  217. Towards expert-level medical question answering with large language models. arXiv preprint arXiv:2305.09617 .
  218. Fake news data sets. https://www.kaggle.com/datasets/bjoernjostein/fake-news-data-set?resource=downloa.
  219. On early detection of hallucinations in factual question answering. arXiv preprint arXiv:2312.14183 .
  220. Biomedical knowledge graph-enhanced prompt generation for large language models. arXiv preprint arXiv:2311.17330 .
  221. HaRiM+: Evaluating summary quality with hallucination risk, in: He, Y., Ji, H., Li, S., Liu, Y., Chang, C.H. (Eds.), Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Association for Computational Linguistics, Online only. pp. 895--924. URL: https://aclanthology.org/2022.aacl-main.66.
  222. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32.
  223. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 .
  224. Ai model gpt-3 (dis) informs us better than humans. Science Advances 9, eadh1850.
  225. Towards real-time text-driven image manipulation with unconditional diffusion models. arXiv preprint arXiv:2304.04344 .
  226. A systematic literature review on the effectiveness of deepfake detection techniques. Journal of Cyber Security Technology 7, 83--113.
  227. Ai-synthesized voice detection using neural vocoder artifacts, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 904--912.
  228. End-to-end anti-spoofing with rawnet2, in: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. pp. 6369--6373.
  229. Multi-agent collaboration: Harnessing the power of intelligent llm agents. arXiv:2306.03314.
  230. Detecting cross-modal inconsistency to defend against neural fake news, in: Webber, B., Cohn, T., He, Y., Liu, Y. (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online. pp. 2081--2106. URL: https://aclanthology.org/2020.emnlp-main.163, doi:10.18653/v1/2020.emnlp-main.163.
  231. Naturalspeech: End-to-end text-to-speech synthesis with human-level quality. IEEE Transactions on Pattern Analysis and Machine Intelligence .
  232. The facts of fake news: A research review. Sociology Compass 13, e12724.
  233. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 .
  234. Fine-tuning language models for factuality. arXiv preprint arXiv:2311.08401 .
  235. A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv:2401.01313 .
  236. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 .
  237. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 .
  238. Deepfaking it: America’s 2024 election collides with ai boom. Reuters URL: https://www.reuters.com/world/us/deepfaking-it-americas-2024-election-collides-with-ai-boom-2023-05-30/.
  239. Attention is all you need. Advances in neural information processing systems 30.
  240. Analyzing the source and target contributions to predictions in neural machine translation. arXiv preprint arXiv:2010.10907 .
  241. Freshllms: Refreshing large language models with search engine augmentation. arXiv preprint arXiv:2310.03214 .
  242. Vigc: Visual instruction generation and correction. arXiv preprint arXiv:2308.12714 .
  243. Neural codec language models are zero-shot text to speech synthesizers. arXiv preprint arXiv:2301.02111 .
  244. Mitigating fine-grained hallucination by fine-tuning large vision-language models with caption rewrites, in: International Conference on Multimedia Modeling, Springer. pp. 32--45.
  245. Noise based deepfake detection via multi-head relative-interaction, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 14548--14556.
  246. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171 .
  247. Hallucination detection for generative large language models by bayesian sequential estimation, in: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15361--15371.
  248. Knowledge graph prompting for multi-document question answering. arXiv preprint arXiv:2308.11730 .
  249. Lm-vc: Zero-shot voice conversion via speech generation based on language models. IEEE Signal Processing Letters .
  250. Implementing bert and fine-tuned roberta to detect ai generated news by chatgpt. arXiv preprint arXiv:2306.07401 .
  251. Jailbroken: How does llm safety training fail? Advances in Neural Information Processing Systems 36.
  252. Simple synthetic data reduces sycophancy in large language models. arXiv preprint arXiv:2308.03958 .
  253. Visual disinformation in a digital age: A literature synthesis and research agenda. new media & society 25, 3696--3713.
  254. Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models. arXiv preprint arXiv:2308.09729 .
  255. The emergence of deepfake technology: A review. Technology innovation management review 9.
  256. “all around me are synthetic faces”: the mad world of ai-generated media. IT Professional 22, 90--99.
  257. A latent space of stochastic diffusion models for zero-shot image editing and guidance, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7378--7387.
  258. Fake news in sheep’s clothing: Robust fake news detection against llm-empowered style attacks. arXiv preprint arXiv:2310.10830 .
  259. A survey on llm-gernerated text detection: Necessity, methods, and future directions. arXiv preprint arXiv:2310.14724 .
  260. Continual learning for large language models: A survey. arXiv:2402.01364.
  261. Ragtruth: A hallucination corpus for developing trustworthy retrieval-augmented language models. arXiv preprint arXiv:2401.00396 .
  262. The rise and potential of large language model based agents: A survey. arXiv:2309.07864.
  263. Efuf: Efficient fine-grained unlearning framework for mitigating hallucinations in multimodal large language models. arXiv preprint arXiv:2402.09801 .
  264. Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms. arXiv preprint arXiv:2306.13063 .
  265. Controllable video generation by learning the underlying dynamical system with neural ode. arXiv preprint arXiv:2303.05323 .
  266. Asvspoof 2021: accelerating progress in spoofed and deepfake speech detection, in: ASVspoof 2021 Workshop-Automatic Speaker Verification and Spoofing Coutermeasures Challenge.
  267. Vigor: Improving visual grounding of large vision language models with fine-grained reward modeling. arXiv preprint arXiv:2402.06118 .
  268. Paint by example: Exemplar-based image editing with diffusion models, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18381--18391.
  269. One-shot domain adaptation for face generation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 5921--5930.
  270. Diffsound: Discrete diffusion model for text-to-sound generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing .
  271. Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms. arXiv preprint arXiv:2401.11708 .
  272. Diffusion probabilistic modeling for video generation. arXiv:2203.09481.
  273. A new benchmark and reverse validation method for passage-level hallucination detection, in: Bouamor, H., Pino, J., Bali, K. (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore. pp. 3898--3908. URL: https://aclanthology.org/2023.findings-emnlp.256, doi:10.18653/v1/2023.findings-emnlp.256.
  274. A robust audio deepfake detection system via multi-view feature. arXiv preprint arXiv:2403.01960 .
  275. Design of a multi-dimensional model for ugc-based emotion analysis, in: 2019 International Conference on Computational Science and Computational Intelligence (CSCI), IEEE. pp. 1383--1387.
  276. Add 2023: the second audio deepfake detection challenge. arXiv preprint arXiv:2305.13774 .
  277. Audio deepfake detection: A survey. arXiv preprint arXiv:2308.14970 .
  278. Woodpecker: Hallucination correction for multimodal large language models. arXiv preprint arXiv:2310.16045 .
  279. Do large language models know what they don’t know? arXiv preprint arXiv:2305.18153 .
  280. Legal prompting: Teaching a language model to think like a lawyer. arXiv preprint arXiv:2212.01326 .
  281. A survey on deepfake video detection. Iet Biometrics 10, 607--624.
  282. Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data. arXiv preprint arXiv:2311.13614 .
  283. Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback. ArXiv abs/2312.00849. URL: https://api.semanticscholar.org/CorpusID:265608723.
  284. Improving language models via plug-and-play retrieval feedback. arXiv preprint arXiv:2305.14002 .
  285. The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. Journal of Data and Information Quality (JDIQ) 11, 1--37.
  286. Defending against neural fake news. Advances in neural information processing systems 32.
  287. IST-unbabel 2021 submission for the quality estimation shared task, in: Barrault, L., Bojar, O., Bougares, F., Chatterjee, R., Costa-jussa, M.R., Federmann, C., Fishel, M., Fraser, A., Freitag, M., Graham, Y., Grundkiewicz, R., Guzman, P., Haddow, B., Huck, M., Yepes, A.J., Koehn, P., Kocmi, T., Martins, A., Morishita, M., Monz, C. (Eds.), Proceedings of the Sixth Conference on Machine Translation, Association for Computational Linguistics, Online. pp. 961--972. URL: https://aclanthology.org/2021.wmt-1.102.
  288. Alignscore: Evaluating factual consistency with a unified alignment function. arXiv preprint arXiv:2305.16739 .
  289. Halle-switch: Controlling object hallucination in large vision language models. arXiv e-prints , arXiv--2310.
  290. R-tuning: Teaching large language models to refuse unknown questions. arXiv preprint arXiv:2311.09677 .
  291. Video-llama: An instruction-tuned audio-visual language model for video understanding. arXiv:2306.02858.
  292. Adding conditional control to text-to-image diffusion models, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3836--3847.
  293. How language model hallucinations can snowball. arXiv preprint arXiv:2305.13534 .
  294. An overview of online fake news: Characterization, detection, and discussion. Information Processing & Management 57, 102025.
  295. Siren’s song in the ai ocean: a survey on hallucination in large language models. arXiv preprint arXiv:2309.01219 .
  296. Controlvideo: Training-free controllable text-to-video generation. arXiv preprint arXiv:2305.13077 .
  297. Sine: Single image editing with text-to-image diffusion models, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6027--6037.
  298. Multi-attentional deepfake detection, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 2185--2194.
  299. Mitigating object hallucination in large vision-language models via classifier-free guidance. arXiv preprint arXiv:2402.08680 .
  300. Hallucination detection for grounded instruction generation, in: Bouamor, H., Pino, J., Bali, K. (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore. pp. 4044--4053. URL: https://aclanthology.org/2023.findings-emnlp.266, doi:10.18653/v1/2023.findings-emnlp.266.
  301. Verify-and-edit: A knowledge-enhanced chain-of-thought framework, in: Rogers, A., Boyd-Graber, J., Okazaki, N. (Eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Toronto, Canada. pp. 5823--5840. URL: https://aclanthology.org/2023.acl-long.320, doi:10.18653/v1/2023.acl-long.320.
  302. Emofake: An initial dataset for emotion fake audio detection. arXiv preprint arXiv:2211.05363 .
  303. Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization. arXiv preprint arXiv:2311.16839 .
  304. Minigpt-5: Interleaved vision-and-language generation via generative vokens. arXiv preprint arXiv:2310.02239 .
  305. Neural deepfake detection with factual structure of text, in: Webber, B., Cohn, T., He, Y., Liu, Y. (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Online. pp. 2461--2470. URL: https://aclanthology.org/2020.emnlp-main.193, doi:10.18653/v1/2020.emnlp-main.193.
  306. Lima: Less is more for alignment. Advances in Neural Information Processing Systems 36.
  307. Detecting hallucinated content in conditional neural sequence generation. arXiv preprint arXiv:2011.02593 .
  308. A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR) 53, 1--40.
  309. Aligning modalities in vision large language models via preference fine-tuning. arXiv preprint arXiv:2402.11411 .
  310. Analyzing and mitigating object hallucination in large vision-language models. arXiv preprint arXiv:2310.00754 .
  311. Genimage: A million-scale benchmark for detecting ai-generated image. Advances in Neural Information Processing Systems 36.
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