Abstract

One popular digital image forgery technique for identifying regions of image forgery is Copy-Move Forgery Detection (CMFD). Copy-move forging is the procedure of attaching a specific section of an image to a new element of an identical image to replicate the forged image elements as an original. The fake appears realistic because the image preserves all the fundamental characteristics of the original image, even after its creation in the target region. Because editing tools and image capture have been more widely available, the quantity of phony photographs on the internet has exploded. Further, social media and other networks have emerged as the primary means of distributing modified photos, rumors, fake news, and other such content. Therefore, creating efficient methods for identifying these forgeries has become crucial. The Copy Move Forgery (CMF), which uses the patches inside the image to change it, is among the most prevalent kinds of forgeries. Deep structured learning-based methods generally perform better but have a focus on generalization. Besides feature matching, this paper also proposes a new deep-learning algorithm to detect forgeries. First, the images from the standard dataset are collected. Preprocessing methods include Retinex and Contrast Limited Adaptive Histogram Equalization (CLAHE) are applied. Further, each of the collected images is subjected to post-processing to improve image effectiveness. The pre-processed images are then fed into an Efficient Convolutional Transformer with Spatial Attention Network (ECT-SANet) for feature extraction. Then we perform the feature matching operation using the Weighted Multi-Similarity Check (WMS) method. The Adaptive Threshold is optimized by Randomized Improved Orca Predation Algorithm (RE-OPA). The matched features are then filtered for false-positives using a Random Sample Consensus (RANSAC) algorithm. The performance of the proposed approach is evaluated in terms of the CMFD.

Keywords

Copy-Move Forgery Detection, Contrast Limited Adaptive Histogram Equalization, Efficient Convolutional Transformer with Spatial Attention Network, Weighted Multi-Similarity Check and Adaptive Thresholding, Randomized Enhanced Orca Predation Algorithm,

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