A Survey on Deep-Learning-Based Techniques for Detecting AI-Generated Synthetic Images
Staycy Guevara, Ana Lucila Sandoval Orozco, Luis Javier García Villalba · 2026
Detecting synthetic images has become increasingly challenging due to the high realism achieved by current generation models. Generative adversarial networks (GANs) and diffusion models can produce images that mimic human features and textures with remarkable accuracy, raising concerns about the spread of sensitive content, such as AI-generated child sexual abuse material (CSAM). To address this issue, deep-learning-based detection techniques can accurately distinguish AI-generated images from real ones, offering robust generalization capabilities. This review provides an in-depth examination of AI-generated synthetic image detection techniques, highlighting strengths, limitations, and emerging trends, with a focus on applications in detecting manipulated content and identifying areas for future research and development.