Combating Digitally Altered Images: Deepfake Detection

• URL: http://arxiv.org/abs/2508.16975v1
• Date: Sat, 23 Aug 2025 09:59:03 GMT
• Title: Combating Digitally Altered Images: Deepfake Detection
• Authors: Saksham Kumar, Rhythm Narang,
• Abstract summary: This study presents a robust Deepfake detection based on a modified Vision Transformer(ViT) model.<n>The model has been trained on a subset of the OpenForensics dataset with multiple augmentation techniques to increase robustness for diverse image manipulations.<n>The model demonstrates state-of-the-art results on the test dataset to meticulously detect Deepfake images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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