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National Institute of Technology, Karnataka

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Research library15linked papers
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Selected work

Representative Papers

Towards Generalizable Deepfake Image Detection with Vision Transformers

Apr 19, 2026

This work addresses the limited generalization of existing deepfake detection methods in the face of rapidly evolving generative models and diverse forgery techniques. It proposes a robust detection system by integrating fine-tuned state-of-the-art vision Transformer models—DINOv2, AIMv2, and OpenCLIP ViT-L/14—and optimizes them on the large-scale in-the-wild dataset DF-Wild. Evaluated on the DF-Wild test set, the proposed approach achieves an AUC of 96.77% and an EER of 9%, outperforming the current best method by 7.05% in AUC and 8% in EER. This significant improvement in detecting unseen forgery types earned the method first place in the IEEE S&P Cup 2025.

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Retinal Cyst Detection from Optical Coherence Tomography Images

Apr 12, 2026

Current automated methods for retinal cyst segmentation exhibit limited accuracy (only 68%) and insufficient robustness on high-noise OCT images—particularly those acquired with Topcon devices—hindering their clinical utility for precise quantification. To address this, this work proposes a ResNet-based patch classification strategy and conducts training and evaluation on a publicly available challenge dataset encompassing multi-vendor imaging systems and annotations from multiple experts. It presents the first systematic analysis of generalization performance across different OCT devices for cyst segmentation. The proposed method achieves Dice scores exceeding 70% across all vendors, significantly outperforming existing state-of-the-art approaches and demonstrating markedly improved segmentation accuracy and robustness to variations in image quality.

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Recent publications

Latest Papers

Towards Generalizable Deepfake Image Detection with Vision Transformers

Apr 19, 2026

This work addresses the limited generalization of existing deepfake detection methods in the face of rapidly evolving generative models and diverse forgery techniques. It proposes a robust detection system by integrating fine-tuned state-of-the-art vision Transformer models—DINOv2, AIMv2, and OpenCLIP ViT-L/14—and optimizes them on the large-scale in-the-wild dataset DF-Wild. Evaluated on the DF-Wild test set, the proposed approach achieves an AUC of 96.77% and an EER of 9%, outperforming the current best method by 7.05% in AUC and 8% in EER. This significant improvement in detecting unseen forgery types earned the method first place in the IEEE S&P Cup 2025.

0 citationsRead paper

Retinal Cyst Detection from Optical Coherence Tomography Images

Apr 12, 2026

Current automated methods for retinal cyst segmentation exhibit limited accuracy (only 68%) and insufficient robustness on high-noise OCT images—particularly those acquired with Topcon devices—hindering their clinical utility for precise quantification. To address this, this work proposes a ResNet-based patch classification strategy and conducts training and evaluation on a publicly available challenge dataset encompassing multi-vendor imaging systems and annotations from multiple experts. It presents the first systematic analysis of generalization performance across different OCT devices for cyst segmentation. The proposed method achieves Dice scores exceeding 70% across all vendors, significantly outperforming existing state-of-the-art approaches and demonstrating markedly improved segmentation accuracy and robustness to variations in image quality.

0 citationsRead paper