Deepfake detection aims to mitigate the threat of manipulated content by identifying and
exposing forgeries. However, previous methods primarily tend to perform poorly when …

The rapid progress in generative models has given rise to the critical task of AI-Generated
Content Stealth (AIGC-S), which aims to create AI-generated images that can evade both …

The increase in face manipulation models has led to a critical issue in society—the synthesis
of realistic visual media. With the emergence of new forgery approaches at an …

Generative adversarial networks have shown impressive results in the modeling of movies
and games, but what if such powerful image generation capability is used to harm the …

Deep generator technology can produce high-quality fake videos that are indistinguishable,
posing a serious social threat. Traditional forgery detection methods directly centralized …

Previous deepfake detection methods mostly depend on low-level textural features
vulnerable to perturbations and fall short of detecting unseen forgery methods. In contrast …

As the saying goes," seeing is believing". However, with the development of digital face
editing tools, we can no longer trust what we can see. Although face forgery detection has …

Existing face forgery detection methods often consider the manipulation detection problem
as a binary classification problem, which are easily prone to overfitting. The detection …

The malicious use of deepfake technologies poses a significant threat to social security,
emphasizing the urgent necessity to advance deepfake detection. Existing detection models …

Facial forgery by deepfakes has caused major se-curity risks and raised severe societal
concerns. As a counter-measure, a number of deepfake detection methods have been …