UNMASKING DEEPFAKE FACE SWAPS USING MULTI-RESOLUTION HYBRID DCT-DWT APPROACH

In todays digital world, the advanced tools for image editing using AI technologies have made it harder to identify whether an image is real or fake. Deepfake face swaps are the most convincing forms of image forgery among different manipulation formats. This study presents a new hybrid method that combines the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT) to detect such deepfakes. By breaking the image into different frequency and spatial layers, the proposed method can easily detect the visual inconsistencies happened due to the manipulations. By combining both DCT and DWT, the manipulated regions can be efficiently located. The experiment shows that the proposed hybrid model improves both accuracy and computational performance. It offers a robust and reliable framework for digital image authenticity against the growing deepfake technologies.