Image Forensics

Image forensics aims to verify the authenticity of digital images and videos, detecting manipulations like splicing, copy-move forgery, and deepfakes. Current research heavily utilizes deep learning, employing architectures such as convolutional neural networks (CNNs) and transformers, often incorporating techniques like error level analysis and feature extraction from metadata (e.g., EXIF data) to identify subtle manipulation traces. This field is crucial for combating misinformation and ensuring the integrity of digital evidence in legal and journalistic contexts, driving advancements in both algorithm design and the creation of comprehensive benchmark datasets.