Leveraging DeepLabV3 for Accurate Localization of Image Forgeries

With the exponential growth of digital media, the need for robust techniques to detect and localize image forgeries has become critical to ensuring media integrity. This research proposes a new method that utilizes DeepLabV3, a cutting-edge deep learning model designed for semantic segmentation, to effectively and precisely identify and locate image forgeries within media databases. By exploiting the model’s ability to capture multiscale context through atrous spatial pyramid pooling, our method precisely identifies tampered regions in diverse and complex datasets. The framework incorporates advanced preprocessing pipelines and binary cross-entropy loss optimization to enhance the segmentation accuracy of forged regions. Comprehensive experiments conducted on the CASIA v2 dataset, which contains a wide range of forged images and their corresponding masks, demonstrate the effectiveness of our approach, achieving high performance in terms of Intersection over Union (IoU), accuracy, and F1 score. The proposed model, leveraging DeepLabV3, outperforms with an IoU of 0.90, F1-score of 0.94, accuracy of 0.99, and AUC of 0.99. This research offers significant advancements in the field of image forensics, paving the way for reliable and automated detection mechanisms in digital forensics and media authenticity validation.