Image forgery detection is the process of identifying manipulated or altered images to determine their authenticity, ensuring the integrity of digital media. Image forgery was predicted to maintain trust in digital media, prevent misinformation, and ensure the credibility of visual evidence in various fields like journalism and law enforcement. Traditional methods for predicting image forgery relied on manual inspection and basic image processing methods, those techniques were time-consuming, required expert knowledge, and were prone to human error, making them less reliable and efficient. Recently presented Artificial Intelligence (AI) techniques, automate the detection process by learning from large datasets, offering increased accuracy and faster processing times. However, such methods need large amounts of labelled data and a good amount of computational power to train the models properly. To address these limitations, proposed Global Convolutional Context Networks (GCCNet) were employed to identify a fake images. The image is first taken from a dataset that was used to detect copy moves. The Quantum Wavelet Transform Filter (QWTF) and RetiNex algorithm are used to pre-process the input image. That is the original image’s noise is eliminated using QWTF, and the pixel contrast is improved using RetiNex. The copied portion of the pre-processed images is then segmented using the Pyramid Scene Parsing Network (CAP-PSPNet), which is based on Contour Aware Processing to make the segmentation edges sharper. The image’s segmented portion is then passed to a hybrid Attention-based Shuffle-Net V2 (ATSNetV2) with Global Convolutional Context Networks (GCCNet) to predict real or fake images. As a result, the model attained an accuracy of 96.60%, an error rate of 3.40%, and F1-score of 96.7%. These highlight the proposed model offer high precision and reliability in image forgery detection.
Khumallambam et al. (Sun,) studied this question.