Enhancing Image Forgery Detection with Multi-Modal Deep Learning and Statistical Methods

The manipulation of digital images from journalism to social media and in forensics has made detection of image forgery a significant area of research. Techniques for forgery detection are generally classified into three categories: splicing, copy-move, and retouching. The mainstay of the classic methods is handcrafted features which range from resampling artefacts to edge inconsistencies and finally DCT coefficients that point towards anomalies. However, with deep learning, this domain has totally transformed: it is possible to learn complex patterns straight from pixel data to get even more sophisticated detection. Modern approaches rely on convolutional neural networks (CNNs) and prefabricated architectures such as ResNet50 and VGG16 to embrace both global and local inconsistency in images. Hybrid models combining the capabilities from deep learning and statistical methods have also been found to perform better than others. With all these advances, however, several problems still exist. It is challenging to produce subtle forgeries that survive most post-processing procedures, such as compression and resizing. More generalizable models, along with the designs they are intended to build upon, should be developed for the detection of various kinds of forgeries in diverse image datasets and reflect real challenges in diverse real-world scenarios.