Biblio

Fake news, frequently making use of tampered photos, has currently emerged as a global epidemic, mainly due to the widespread use of social media as a present alternative to traditional news outlets. This development is often due to the swiftly declining price of advanced cameras and phones, which prompts the simple making of computerized pictures. The accessibility and usability of picture-altering softwares make picture-altering or controlling processes significantly simple, regardless of whether it is for the blameless or malicious plan. Various investigations have been utilized around to distinguish this sort of controlled media to deal with this issue. This paper proposes an efficient technique of copy-move forgery detection using the deep learning method. Two deep learning models such as Buster Net and VGG with FPN are used here to detect copy move forgery in digital images. The two models' performance is evaluated using the CoMoFoD dataset. The experimental result shows that VGG with FPN outperforms the Buster Net model for detecting forgery in images with an accuracy of 99.8% whereas the accuracy for the Buster Net model is 96.9%.

Large number of digital images and videos are acquired, stored, processed and shared nowadays. High quality imaging hardware and low cost, user friendly image editing software make digital mediums vulnerable to modifications. One of the most popular image modification techniques is copy move forgery. This tampering technique copies part of an image and pastes it into another part on the same image to conceal or to replicate some part of the image. Researchers proposed many techniques to detect copy move forged regions of images recently. These methods divide image into overlapping blocks and extract features to determine similarity among group of blocks. Selection of the feature extraction algorithm plays an important role on the accuracy of detection methods. Column averages of 1D-FT of rows is used to extract features from overlapping blocks on the image. Blocks are transformed into frequency domain using 1D-FT of the rows and average values of the transformed columns form feature vectors. Similarity of feature vectors indicates possible forged regions. Results show that the proposed method can detect copy pasted regions with higher accuracy compared to similar works reported in the literature. The method is also more resistant against the Gaussian blurring or JPEG compression attacks as shown in the results.