| dc.description.abstract | With the growing accessibility of image editing tools and deep learning-based forgery applications, individuals can easily alter images and videos, disseminating them across social media networks. Such tampered images and forged videos not only create confusion but can also cause irreversible damage to personal reputation and privacy. In response, numerous detection methods for forged images and deepfake videos have been developed in recent years. These methods often rely on training with datasets containing specific tampering techniques to create targeted detection mechanisms. However, as forgery technologies advance, new and unknown tampering methods may emerge. Additionally, tampered images and videos may undergo compression or encoding during dissemination, which can obscure tampering traces, diminishing the effectiveness of current detection methods.
This study introduces a deep learning-based forgery detection method that utilizes block consistency to address the challenge of diminished tampering traces in compressed images and encoded videos. By evaluating the similarity of block content within the images or videos, this method determines whether tampering has occurred. Unlike existing approaches that train on the datasets with target tampering operations, our method uses general image data to train the deep learning model, thereby enhancing the model’s generalization capability. The proposed scheme was formed by first developing a feature extractor using convolutional neural networks to identify the source of the images and then employing a Siamese network to classify image compression levels. For image tampering detection, heatmap transformations and foreground extraction were used to pinpoint tampered areas. In deepfake video detection, we concentrated on facial regions, assessing the similarity between consecutive frames to verify the video’s authenticity. The effectiveness of this method was validated and tested using publicly available datasets, which include a range of tampered images and outputs from deepfake video models. The strong performance of the proposed block consistency method underscores its potential in enhancing image tampering detection and deepfake video identification. | en_US |