Benefiting from the development of Artificial Intelligence Generated Content (AIGC) technology, facial manipulation has been one of research hotspots in recent years and has achieved significant success in industry. The manipulated faces have become more and more realistic.

However, facial images have been extensively utilized in identification and authentication services. New applications, such as face payment, face retrieval, and face check-in, have emerged, entering daily life in an all-round way. Then, more and more malicious users use the deepfake technology to generate high-quality facial images for malicious purposes. Therefore, research on active defense and forensics against face deepfake is of great significance in combating malicious activities. Active defense refers to protecting the upcoming release of natural faces by destroying the output of deepfake, thereby preventing the forgery behavior at a certain level from the source. Forensic technology is a passive defense technology that detects whether the face has been tampered with. Although existing active defense and forensic technologies have achieved significant progress against deepfake, they still suffer from ordinary performance in black-box scenarios.

This Special Issue will provide a significant collective contribution to the field of active defense and forensics against deepfake and focuses on data-driven approaches using deep learning. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Deepfake active defense
• Adversarial attacks against deepfake
• Deepfake detection
• Face manipulation detection and localization
• Face liveness detection
• Active forensic against deepfake
• Deepfake spoofing attacks
• New deepfake technology
• New adversarial attack technology
• Protection of portrait rights
• Challenges for active defense and forensics against deepfake