Low Quality Deepfake

Low-quality deepfakes, while less visually convincing than their high-quality counterparts, pose a significant challenge to current detection methods due to their variability and the ease with which they can be generated. Research focuses on developing robust detection models using multimodal analysis (combining visual and audio cues), wavelet transforms to capture subtle image features, and techniques that exploit inconsistencies in temporal features or inconsistencies introduced by the deepfake generation process itself. The ability to reliably detect these forgeries is crucial for mitigating the spread of misinformation and protecting against various forms of fraud and identity theft, driving ongoing efforts to improve detection accuracy and generalizability across diverse deepfake creation methods.