Detecting Multimedia Generated by Large AI Models: A Survey

Abstract: The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and LLMs, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life. Although beneficial in numerous fields, this content presents significant risks, including potential misuse, societal disruptions, and ethical concerns. Consequently, detecting multimedia generated by LAIMs has become crucial, with a marked rise in related research. Despite this, there remains a notable gap in systematic surveys that focus specifically on detecting LAIM-generated multimedia. Addressing this, we provide the first survey to comprehensively cover existing research on detecting multimedia (such as text, images, videos, audio, and multimodal content) created by LAIMs. Specifically, we introduce a novel taxonomy for detection methods, categorized by media modality, and aligned with two perspectives: pure detection (aiming to enhance detection performance) and beyond detection (adding attributes like generalizability, robustness, and interpretability to detectors). Additionally, we have presented a brief overview of generation mechanisms, public datasets, online detection tools, and evaluation metrics to provide a valuable resource for researchers and practitioners in this field. Most importantly, we offer a focused analysis from a social media perspective to highlight their broader societal impact. Furthermore, we identify current challenges in detection and propose directions for future research that address unexplored, ongoing, and emerging issues in detecting multimedia generated by LAIMs. Our aim for this survey is to fill an academic gap and contribute to global AI security efforts, helping to ensure the integrity of information in the digital realm. The project link is https://github.com/Purdue-M2/Detect-LAIM-generated-Multimedia-Survey.

• The paper introduces a taxonomy that classifies detection methods for AI-generated text, images, video, audio, and multimodal content.
• It reviews approaches such as stylistic analysis, watermarking, and feature extraction while addressing issues of dataset diversity and detection reliability.
• The study emphasizes enhancing generalizability and robustness in detectors through standardized metrics and advanced training paradigms.

Detecting Multimedia Generated by Large AI Models: An Analytical Survey

The surveyed paper meticulously explores the challenges and methodologies pertinent to the detection of multimedia generated by large AI models (LAIMs), with a focus on diffusion models and LLMs. The ascension of LAIMs has fostered significant advancements across numerous domains, yet their potential for misuse underscores an urgent need for robust detection mechanisms. Through a systematic survey, the paper offers a novel taxonomy by categorizing detection methods based on functionality across multiple modalities, namely text, image, video, audio, and multimodal content.

Overview and Taxonomy

1. Text Detection: The detection of LLM-generated text bifurcates into methods prioritizing detection accuracy (Pure Detection) and methods enhancing the detection process with additional characteristics (Beyond Detection). Techniques include stylistic and coherence analysis and watermarking under pure methods, while beyond methods explore generalization, robustness, and attribution.
2. Image Detection: Techniques focus on detecting artifacts in diffusion model-generated images leveraging both physical/physiological cues and diverse feature extraction methods, with a significant emphasis placed on generalizability, interpretability, and localizability of detections.
3. Video and Audio Detection: Emerging areas compared to text and image detection. The limited scope primarily addresses generalizability within video detection and artifact detection in audio forged by neural vocoders.
4. Multimodal Detection: Utilizes multiple data modalities to detect forgeries, emphasizing the integration of text and image features for improved detection performance. Beyond detection methodologies explore interpretability, generalization, localization, and text-image inconsistency.

Empirical Evaluations

The survey thoroughly describes public datasets such as HC3, GenImage, and $DGM^4$ tailored for detecting LAIM-generated content, explicitly elucidating each dataset's scope and limitations. However, the survey identifies substantial challenges in dataset diversity and size, especially for audio, video, and multimodal tasks.

Challenges and Future Directions

The paper elucidates pressing challenges including the constrained reliability of existing detectors due to limited generalizability, interpretability, and robustness. These challenges underscore the necessity for foundation models and the importance of embracing discrepancies inherent in generative processes to enhance detection reliability.

Future research avenues highlighted involve leveraging generalizable foundation models, facilitating robust training paradigms to uplift detector reliability, addressing biases in detection tasks, particularly across demographic groups, and accelerating the development of robust multimodal detectors and datasets.

Conclusion

This survey significantly contributes to global AI security efforts by furnishing a comprehensive review of existing detection techniques and the hurdles they encounter. By articulating future research directions, this work serves as a vital resource for researchers aiming to mitigate potential risks associated with LAIM-generated multimedia, fostering a secure and informed digital ecosystem. The survey also stresses the importance of standardized evaluation metrics to enable fair comparisons of different techniques and tools, emphasizing the critical need for a unified benchmark in media forensics. This work thus lays a robust groundwork for future advancements in detecting increasingly sophisticated forgeries from large AI models.