Recent years have seen a substantial increase in interest in deepfakes, a fast-developing
field at the nexus of artificial intelligence and multimedia. These artificial media creations …

In this paper, we consider the face swapping detection from the perspective of face identity.
Face swapping aims to replace the target face with the source face and generate the fake …

In recent years, face forgery detectors have aroused great interest and achieved impressive
performance, but they are still struggling with generalization and robustness. In this work, we …

DeepFake detection aims to differentiate falsified faces from real ones. Most approaches
formulate it as a binary classification problem by solely mining the local artifacts and …

Face forgery detection is essential in combating malicious digital face attacks. Previous
methods mainly rely on prior expert knowledge to capture specific forgery clues, such as …

Recent studies on face forgery detection have shown satisfactory performance for methods
involved in training datasets, but are not ideal enough for unknown domains. This motivates …

With the continuous development of deep learning in the field of image generation models, a
large number of vivid forged faces have been generated and spread on the Internet. These …

Recent advancements in language-image models have led to the development of highly
realistic images that can be generated from textual descriptions. However, the increased …

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection
methods are typically inadequate in generalizability, with a tendency to overfit to image …

Image manipulation localization (IML), which seeks to accurately segment tampered regions
that are artfully fastened into a normal image, is a fundamental yet challenging computer …