Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
This work addresses image forgery detection for forensic applications, representing an incremental advancement by adapting existing descriptors to deep learning methods.
The paper tackled the problem of improving image forgery detection by reformulating residual-based local descriptors as constrained convolutional neural networks (CNNs) and then fine-tuning them, resulting in a significant performance improvement over conventional detectors.
Local descriptors based on the image noise residual have proven extremely effective for a number of forensic applications, like forgery detection and localization. Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning. In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN). Then, by relaxing the constraints, and fine-tuning the net on a relatively small training set, we obtain a significant performance improvement with respect to the conventional detector.