Deepfake Image

Deepfake images, synthetically generated images designed to convincingly mimic real individuals, pose a significant threat to information integrity and security. Current research focuses on developing robust detection methods, exploring various architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and hybrid quantum-classical models, often leveraging frequency domain analysis and attention mechanisms to identify subtle inconsistencies between real and fake images. A key challenge lies in improving the generalization of detectors across diverse deepfake generation methods and image qualities (e.g., compression artifacts, moiré patterns), with recent work emphasizing the importance of temporal consistency analysis in videos and the development of explainable detection frameworks. The development of effective and generalizable deepfake detection is crucial for mitigating the spread of misinformation and protecting against malicious applications.