Forgery Image

Forgery image detection research focuses on developing robust and generalizable methods to identify manipulated or synthetically generated images, addressing the growing threat of misinformation and fraud. Current research emphasizes leveraging powerful pre-trained models like CLIP, incorporating self-supervised learning techniques, and analyzing pixel-level inconsistencies to detect various forgery types, including deepfakes and AI-generated art. These advancements are crucial for safeguarding image authenticity across diverse applications, from media verification to satellite imagery analysis, and contribute to a broader understanding of image manipulation techniques and their detection.