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Article summary:
1. This article proposes a proactive framework for combating DeepFakes before data manipulation.
2. The proposed method inverts face images into latent codes with an auto-encoder and searches for adversarial face embeddings in their neighbor with the gradient descent method.
3. Extensive experiments on three typical DeepFake manipulation methods have demonstrated the effectiveness of the proposed method.
Article analysis:
The article is generally reliable and trustworthy, as it provides evidence to support its claims through extensive experiments on three typical DeepFake manipulation methods. The authors also provide references to other relevant publications, which adds to the credibility of the article. Furthermore, the authors present both sides of the argument equally, providing a balanced view of the topic.
However, there are some potential biases that should be noted. For example, while the authors provide evidence for their claims, they do not explore any counterarguments or alternative solutions to combat DeepFakes. Additionally, there is no discussion about possible risks associated with using this approach or any potential drawbacks that could arise from its implementation. Finally, there is no mention of how this approach could be used in real-world applications or what implications it may have on society at large.