Synthetic Image Detection Across Different Generative Models
Abstract
This paper investigates synthetic digital image detection across different generative models. The task is relevant due to rapid development of generative artificial intelligence, which enables creation of visual content that is difficult to distinguish from real imagery and increases risks related to misinformation, visual forgery, and declining trust in digital media. The study compares several detection approaches: CLIP-based semantic representations, FFT-based frequency-domain features, fusion models combining semantic and frequencydomain information, and ensembles of detectors. Experiments were conducted on tinyGenImage, a subset of GenImage, using BigGAN and Stable Diffusion v1.5 images for training and validation. Performance across different sources was evaluated on images generated by Midjourney, Wukong, and GLIDE, which were not used during training. Results show that CLIP-based models provide a strong baseline, while FFT-only models perform weaker as standalone detectors. Fusion models did not consistently improve over CLIP baselines, whereas ensembling achieved the best overall performance and improved classification quality across different generative sources.