关于Deepfakes的归属
On Attribution of Deepfakes
摘要：
生成建模的进展，特别是生成对抗网络，使得大规模有效合成和改变媒体成为可能。恶意的个人现在依靠这些机器生成的媒体或deepfake来操纵社会话语。为了确保媒体的真实性，现有的研究主要集中在deepfake检测上。然而，用于生成式建模的框架的对抗性表明，在检测deepfake方面取得的进展将使deepfake生成更加真实。因此，生成模型的开发人员受到处理错误信息活动的利益相关者的审查也就不足为奇了。同时，生成模型有很多积极的应用。因此，显然需要开发工具来确保生成建模的透明使用，同时最大限度地减少恶意应用程序造成的危害。我们的技术优化了每个生成模型的熵源，以概率方式将deepfake归因于其中一个模型。我们在人脸合成的开创性示例上评估了我们的方法，证明我们的方法达到了97.62%的归因准确率，并且对扰动和对抗性示例不太敏感。我们讨论了我们的工作的道德影响，确定我们的技术可以在哪里使用，并强调，一个更有意义的立法框架是需要一个更透明和道德的使用生成建模。最后，我们认为，模型开发人员应该能够声称似是而非的可否认性，并提出了第二个框架这样做-这允许模型开发人员出示证据，证明他们没有生产媒体，他们被指控生产。
英文摘要：
Progress in generative modelling, especially generative adversarial networks, have made it possible to efficiently synthesize and alter media at scale. Malicious individuals now rely on these machine-generated media, or deepfakes, to manipulate social discourse. In order to ensure media authenticity, existing research is focused on deepfake detection. Yet, the adversarial nature of frameworks used for generative modeling suggests that progress towards detecting deepfakes will enable more realistic deepfake generation. Therefore, it comes at no surprise that developers of generative models are under the scrutiny of stakeholders dealing with misinformation campaigns. At the same time, generative models have a lot of positive applications. As such, there is a clear need to develop tools that ensure the transparent use of generative modeling, while minimizing the harm caused by malicious applications. Our technique optimizes over the source of entropy of each generative model to probabilistically attribute a deepfake to one of the models. We evaluate our method on the seminal example of face synthesis, demonstrating that our approach achieves 97.62% attribution accuracy, and is less sensitive to perturbations and adversarial examples. We discuss the ethical implications of our work, identify where our technique can be used, and highlight that a more meaningful legislative framework is required for a more transparent and ethical use of generative modeling. Finally, we argue that model developers should be capable of claiming plausible deniability and propose a second framework to do so -- this allows a model developer to produce evidence that they did not produce media that they are being accused of having produced.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
