Previous studies in deepfake detection have shown promising results when testing face
forgeries from the same dataset as the training. However, the problem remains challenging …

Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve
the generalization of DeepFake detection. Existing methods typically generate these faces …

Large numbers of synthesized videos from diffusion models pose threats to information
security and authenticity, leading to an increasing demand for generated content detection …

Recent DeepFake detection methods have shown excellent performance on public datasets
but are significantly degraded on new forgeries. Solving this problem is important, as new …

Three key challenges hinder the development of current deepfake video detection:(1)
Temporal features can be complex and diverse: how can we identify general temporal …

The rapid progress of Deepfake technology has made face swapping highly realistic, raising
concerns about the malicious use of fabricated facial content. Existing methods often …

With the rapid advancement of generative AI, multimodal deepfakes, which manipulate both
audio and visual modalities, have drawn increasing public concern. Currently, deepfake …

Looking at a video sequence where a foreground person is represented is not as time ago
anymore. Deepfakes have revolutionized our way to watch at such contents and nowadays …

Driven by continuous advancements in artificial intelligence, especially deep learning, the
level of realism associated with deepfake technology continues to improve year after year …

One of the serious impacts brought by artificial intelligence is the abuse of deepfake
techniques. Despite the proliferation of deepfake detection methods aimed at safeguarding …