Thanks to ease of using image manipulation programs, copy move forgery is the easiest image modification approach, which aims to duplicate or remove objects in the image. The methods to detect this type of forgeries are partitioned into; block-based, keypoint-based approach that use hand-crafted features. A new deep learning-based forgery detection scheme is presented. An existing trained model AlexNet is utilized to extract feature vectors of image overlapped subblocks. After obtaining features, the similarity between feature vectors has been investigated for the detection and localization of forgery. The method has higher accuracy rate than the considered traditional method in the literature as reported in the obtained test results.

Multimedia devices have become increasingly popular due to high quality and low cost products using advanced technology. These devices can capture multimedia files, which can be modified easily by video editing tools. One of the most frequently encountered forgery types in video forensics is the frame duplication (FD) forgery. Many methods have been proposed in the literature to deal with this type of forgery. These methods do not consider frame‐mirroring (FM) attack which copy a sequence of frames and paste its mirrored versions somewhere else on the same video. A new FD/FM detection method is proposed in this work. The method extracts binary features from frames and determines the similarity among features. Peak‐signal‐to‐noise ratio of the candidate frames is used to eliminate some of the large number of candidates to improve the detection of the forged frames. Experimental results show that the proposed method successfully detects FM/FD attacks and also yields better execution time and detection results compared to similar works reported in the literature.