Defending Against Neural Fake News (1905.12616v3)

Abstract: Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news. Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary's point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover. Given a headline like `Link Found Between Vaccines and Autism,' Grover can generate the rest of the article; humans find these generations to be more trustworthy than human-written disinformation. Developing robust verification techniques against generators like Grover is critical. We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators. We investigate these results further, showing that exposure bias -- and sampling strategies that alleviate its effects -- both leave artifacts that similar discriminators can pick up on. We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.

Overview of "Defending Against Neural Fake News"

The paper "Defending Against Neural Fake News" addresses the emerging challenge of automatically generated disinformation. The primary objective is to examine the potential risks posed by advances in natural language generation (NLG) techniques, specifically in the context of creating realistic and convincing fake news. The paper outlines the development and assessment of a new generative model, termed Grover, alongside the evaluation of verification mechanisms to detect neural fake news.

Key Contributions

1. Threat Modeling Approach: The authors draw a parallel to computer security, employing a threat modeling framework to investigate neural fake news. This framework helps to anticipate possible adversarial actions and develop robust defensive measures against them.
2. Grover Model: Grover is introduced as a model for controllable text generation. It can produce entire news articles, including metadata like the title, author, and publication date. The paper demonstrates that humans find Grover-generated disinformation more credible than human-written disinformation.
3. Verification Techniques: The paper emphasizes the necessity of robust verification techniques. It was found that Grover itself, when employed as a discriminator, can distinguish between neural fake news and real news with 92% accuracy. This is significantly higher than the 73% accuracy achieved by best current discriminators.
4. Artifact Analysis: The research explores how exposure bias and sampling strategies leave detectable artifacts, which discriminators can leverage to detect fake news. This finding underscores the utility of publicizing strong generative models to enhance detection capabilities.

Results and Findings

• Human Perception:

Evaluations indicated that Grover's machine-generated articles were rated by humans as more plausible than the original human-written disinformation. In terms of trustworthiness, the scores increased when fake news was rewritten by Grover.

• Discrimination Effectiveness:

Grover functions effectively as a discriminative model, outperforming other architectures such as BERT and GPT-2. The model's ability to identify its generative artifacts makes it a valuable tool in combating neural fake news.

• Weak Supervision:

The paper further explores the use of weak supervision when limited examples from an adversarial source are available, showing that observing additional generations from weaker models can significantly improve discrimination.

Implications and Future Directions

Practical Implications:

• Defensive Mechanisms:

Releasing generative models like Grover could be crucial, as they provide the most effective means of identifying neural fake news generated by such models. This approach empowers defensive systems to stay ahead of or keep pace with adversarial capabilities.

• Platform Responsibility:

The paper suggests that platforms should integrate deep neural networks to preemptively scrutinize content, akin to how video platforms filter inappropriate content. However, maintaining human oversight is critical to mitigate false positives and manage inherent model biases.

Theoretical Implications:

• Continued Advancement in Text Generation:

As text generation models evolve, they might adopt traits nullifying current detection strategies, such as using insertion-based techniques or models trained against exposure bias. Future research should anticipate and adapt to these advancements.

• Adaptive Discrimination Models:

The theoretical landscape also invites advancements in integrating knowledge systems into discriminative models. Models capable of verifying entire news articles against known facts could present a formidable barrier to the spread of neural disinformation.

Ethical Considerations:

• Model Release Strategy:

The paper argues that releasing strong generative models is imperative for developing robust defenses. It proposes a cautious release policy, balancing the benefits of exposure for defensive purposes against the risks of misuse.

• Dialogue on ML-based Disinformation:

The authors call for an ongoing discussion about the implications and ethical responsibilities surrounding machine learning models and disinformation. They suggest a framework to guide this conversation and inform future research directions.

Conclusion

The paper "Defending Against Neural Fake News" presents a comprehensive exploration of the threat posed by advances in neural language generation, focusing on the Grover model. It illustrates the dual-use nature of such technologies, providing both potential risks and defenses. The research underscores the importance of proactive threat modeling and the dissemination of generative models to enhance our detection capabilities. While the advancements in NLG hold promise, they also necessitate an ongoing dialogue about ethical considerations and responsible use.