Deep Learning-Based Detection of Digital Image Forgeries and Synthetic Content

The expansion of sophisticated digital forgeries and AI-generated synthetic media, exemplified by deepfakes and GAN-manipulated content, poses a severe threat to information integrity, with over 85% of internet users encountering manipulated imagery every week. This chapter comprehensively examines deep learning-based detection methodologies countering challenges. With an emphasis on architectures like CNNs for anomaly detection and Vision Transformers for contextual analysis, the deep learning-based detection techniques are examined. Methods for identifying image and video forgeries are covered, such as Mask R-CNN, 3D CNNs, and LSTMs. Spectral and physiological analyses on benchmark datasets are used to identify synthetic content. The chapter further addresses adversarial attacks targeting detectors and emphasizes explainable AI (Grad-CAM, LIME) to build trust in forensic tools. Finally, it outlines evolving solutions, including adversarial training, blockchain verification, and multimodal frameworks, which are essential for scalable and trustworthy media authentication.