Leveraging ResNet and U-Net Architectures for Accurate Image Forgery Detection and Localization: A Comprehensive Survey

The area of image forgery localization and detection (IFDL) has received considerable attention because of the increased manipulations in the digital age facilitated by powerful generative AI technology. This survey focuses on recent trends in deep learning techniques, especially multimodal, explainable, and generalizable models built up to 2025. U-Net variants and transformer-based models are the main developments, focusing on forgery trace detection. Deep learning models, especially ResNet and U-Net, have improved image forgery detection and localization due to better feature extraction and semantic segmentation. The review incorporates state-of-the-art approaches employing these models and carries out a critical appraisal of design, evaluation metrics, limitations, and possible extensions. The FakeShield framework brings in a trend change by embracing multi-modal LLMs for explainable detection and localization. New approaches like MMTD-Set have provided a greater cross-domain adaptability during model training. Complementary studies investigate novel paradigms to enhance localization accuracy and robustness against anti-forensic attacks. In-depth analyses emphasize the use of complementary forensic analyses when considering real-life scenarios. This survey is a complete guide for research and practitioners working on designing next-generation image forgery detection systems that are accurate yet explainable.