Transfer Learning Framework Detects Digital Image Forgeries

A recent publication on arXiv introduces a framework for detecting digital image forgery that relies on transfer learning. This method fuses compression-aware feature enhancement with deep convolutional neural networks (CNNs). It employs a hybrid input format that merges RGB images with features derived from compression differences (FDIFF) to emphasize subtle artifacts of manipulation. To enhance classification accuracy, a model-specific adaptive threshold optimization strategy utilizing the Youden Index is implemented, balancing true and false positive rates. The study utilized various pretrained CNN architectures on the CASIA v2.0 dataset. This research tackles the difficulties posed by sophisticated image editing tools in the realms of digital forensics and information security.