Fake Image

Fake image detection research focuses on developing robust methods to distinguish artificially generated images from authentic photographs, driven by concerns about misinformation and malicious use of increasingly realistic synthetic media. Current research emphasizes the use of deep learning models, including convolutional neural networks (CNNs), vision transformers, and vision-language models (VLMs), often incorporating techniques like transfer learning and contrastive learning to improve generalization across diverse generative models. This field is crucial for maintaining the integrity of digital information and has significant implications for various applications, from combating online disinformation to ensuring the authenticity of visual evidence in legal and scientific contexts.