Deepfake Detector

Deepfake detectors aim to distinguish authentic media from artificially generated content, combating the spread of misinformation and fraudulent manipulation. Current research focuses on improving the generalization ability of these detectors across diverse deepfake creation methods and datasets, employing techniques like wavelet transforms, adversarial training, and prototype-based approaches within various neural network architectures (e.g., CNNs, Transformers, CapsuleNets). The development of robust and generalizable deepfake detectors is crucial for safeguarding digital security and maintaining public trust in online information, with ongoing efforts addressing challenges such as adversarial attacks and the evolving sophistication of deepfake generation techniques.