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Protecting information from manipulation is important challenge in current days. Digital images are one of the most popular information representation. Images could be used in several fields such as military, social media, security purposes, intelligence fields, evidences in courts, and newspapers. Digital image forgeries mean adding unusual patterns to the original images that cause a heterogeneity manner in form of image properties. Copy move forgery is one of the hardest types of image forgeries to be detected. It is happened by duplicating part or section of the image then adding again in the image itself but in another location. Forgery detection algorithms are used in image security when the original content is not available. This paper illustrates a new approach for Copy Move Forgery Detection (CMFD) built basically on deep learning. The proposed model is depending on applying (Convolution Neural Network) CNN in addition to Convolutional Long Short-Term Memory (CovLSTM) networks. This method extracts image features by a sequence number of Convolutions (CNVs) layers, ConvLSTM layers, and pooling layers then matching features and detecting copy move forgery. This model had been applied to four aboveboard available databases: MICC-F220, MICC-F2000, MICC-F600, and SATs-130. Moreover, datasets have been combined to build new datasets for all purposes of generalization testing and coping with an over-fitting problem. In addition, the results of applying ConvLSTM model only have been added to show the differences in performance between using hybrid ConvLSTM and CNN compared with using CNN only. The proposed algorithm, when using number of epoch’s equal 100, gives high accuracy reached to 100% for some datasets with lowest Testing Time (TT) time nearly 1 second for some datasets when compared with the different previous algorithms.

Copy-Move Forgery Detection (CMFD) is a key issue of image forensics. A copy-move forgery is a type of image tampering that is created by copying a part of the image and pasting it on another part of the same image to perniciously hide or clone certain regions. This paper presents a new methodology for CMFD in digital images. The proposed algorithm is performed in two successive stages; matching stage and refinement stage. In the matching stage, close morphological operation and Connected Component Labeling (CCL) are used to segment the target image into different objects. The Speeded Up Robust Features (SURF) are extracted from each object and used to build an object catalog. The objects in the catalog are compared to each other, and matched objects are determined. If matched objects exist, the image is categorized as forged image. Otherwise, it is categorized as original image. The refinement stage, on the other hand, is implemented to ensure the originality of the target image. Thus, the candidate image that is classified as original is fed into the refinement stage to certify its originality. In this stage, close and open morphological operations as well as CCL are utilized to obtain the various objects in the image. Afterward, the SURF features are extracted from each object and used to build a new object catalog. The match between the objects in this catalog is obtained. If similar objects are found, the candidate image is classified as forged. Otherwise, the image is categorized as original. The proposed technique is assessed on four popular datasets. The results demonstrate the capability and robustness of the proposed technique in detecting the copy-move forgery under different geometrical attacks. Furthermore, the outcomes show that the suggested technique outperforms the previous CMFD methods in terms of Accuracy and execution time.

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