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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. [ABSTRACT FROM AUTHOR]

To abandon the use of overlapping block division and resist the tampering factor of illumination change, a novel copy-move forgery detection method is proposed based on Maximally Stable Extremal Regions (MSERs) and the Local Intensity Order Pattern (LIOP), that integrates block-based and keypoints-based methods. The method involves the following steps: first, affine transformation invariant MSERs are used to maintain the geometrical transformation invariance and reduce computational complexity; second, LIOP features are used to describe the texture and resist illumination change; and finally, RANdom SAmple Consensus is applied to remove false matches. The experiments indicate that the proposed method has great performance for scaling, rotation and illumination changes. Moreover, the method has the high robustness to Gaussian noise, Gaussian blur and JPEG compression. [ABSTRACT FROM AUTHOR]