article · Journal of Intelligent & Fuzzy Systems
Digital images play a vital role across sectors including military intelligence, legal proceedings, journalism, and social media, yet they remain vulnerable to deliberate manipulation. Copy-move forgery, where a section of an image is duplicated and pasted elsewhere within the same picture, is notably difficult to identify when the original image is unavailable. A deep learning approach combines convolutional neural networks with convolutional long short-term memory networks to recognise this form of forgery. The system extracts visual features through sequential convolution, recurrent memory, and pooling layers before matching elements to identify duplicate content. Evaluated across four standard benchmark datasets as well as combined datasets created to test generalisation, the hybrid model outperformed standalone networks. Operating at up to one hundred percent accuracy on certain datasets, the model completes verification in approximately one second.
Digital images frequently serve as critical evidence in courtrooms, intelligence reporting, and news media, making reliable verification essential. Tampered images can distort public perception and subvert legal or military investigations. Providing rapid, automated detection for copy-move alterations helps verify digital media integrity even when original, unedited reference photographs are inaccessible.
This method is an applied and tested algorithmic tool aimed at digital forensics teams, intelligence agencies, media houses, and legal examiners seeking to authenticate image files. With testing times of approximately one second and high accuracy demonstrated on standard public datasets, the technology sits at an applied validation stage and could potentially be integrated into commercial media verification platforms or forensic toolkits.
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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.
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DOI: 10.3233/jifs-201192
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