Publications

Detection of identity swapping attacks in low-resolution image settings

Akshay Agarwal, Nalini Ratha

Abstract: This research proposes a low-resolution identity swap attack detection algorithm to address the challenge of detecting fake face images in low-resolution videos. The algorithm uses artifacts amplification and classification to handle the lack of information content. Extensive evaluations using multiple databases, resolution settings, and attack types demonstrate the strength and effectiveness of the proposed algorithm in in-the-wild settings. The results show superiority compared to existing state-of-the-art works.

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On Which Data Distribution (Synthetic or Real) We Should Rely for Soft Biometric Classification

Manju RA, Atul Kumar, Akshay Agarwal

Abstract: Gender identification is vital for human-computer interaction and identity search. While "real" facial images are standard in gender classification, synthetic images are gaining attention due to privacy concerns and advancements in generative networks. However, their effectiveness remains unclear. This study evaluates the performance of gender classification networks trained on real vs. synthetic face images. Using 8 DNNs, including CNNs and ViTs, across 4 datasets and 6 image corruptions, we also employ Grad-CAM and t-SNE for interpretability.

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PrecipFormer: Efficient Transformer for Precipitation Downscaling

R. Kumar, T. Sharma, V. Vaghela, S. Jha, A. Agarwal

Abstract: A study on the applications of generative models in secure environments.

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