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arXiv · 2508.16975

Combating Digitally Altered Images: Deepfake Detection

Saksham Kumar·Rhythm Narang
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Abstract

The rise of Deepfake technology to generate hyper-realistic manipulated images and videos poses a significant challenge to the public and relevant authorities. This study presents a robust Deepfake detection based on a modified Vision Transformer(ViT) model, trained to distinguish between real and Deepfake images. The model has been trained on a subset of the OpenForensics Dataset with multiple augmentation techniques to increase robustness for diverse image manipulations. The class imbalance issues are handled by oversampling and a train-validation split of the dataset in a stratified manner. Performance is evaluated using the accuracy metric on the training and testing datasets, followed by a prediction score on a random image of people, irrespective of their realness. The model demonstrates state-of-the-art results on the test dataset to meticulously detect Deepfake images.

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Saksham Kumar, Rhythm Narang. 2025-08-23. Combating Digitally Altered Images: Deepfake Detection. https://arxiv.org/abs/2508.16975

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