Countering synthetic realities: Advances, challenges, and future directions in deepfake image detection

The proliferation of deepfake imagery generated through advanced deep learning techniques presents unprecedented challenges to digital media authenticity and security. This survey offers a systematic and comprehensive examination of contemporary deepfake image detection methodologies, addressing both technical advancements and practical implementation challenges. Beginning with an analysis of evolving generation techniques from GANs to diffusion models, we critically evaluate their implications for detection systems. The paper then provides a structured taxonomy of detection approaches, encompassing traditional forensic methods, deep learning architectures including CNNs and vision transformers, frequency-domain analysis, and innovative hybrid systems. We assess each method's performance characteristics, with particular attention to generalization capabilities across diverse datasets and robustness against adversarial manipulations. The discussion extends to explainability frameworks that enhance detection transparency and trustworthiness. Current challenges in deployment scalability, real-time processing, and bias mitigation are thoroughly examined, alongside emerging solutions such as multimodal fusion and efficient neural architectures. By synthesizing cutting-edge research with practical considerations, this survey not only maps the current landscape but also identifies critical research directions for developing next-generation detection systems capable of countering increasingly sophisticated synthetic media. The analysis serves as an essential reference for researchers and practitioners working at the intersection of computer vision, digital forensics, and media security.