Single-step Adversarial training with Dropout Scheduling

no code implementations • CVPR 2020 • Vivek B. S., R. Venkatesh Babu

In this work, (i) we show that models trained using single-step adversarial training method learn to prevent the generation of single-step adversaries, and this is due to over-fitting of the model during the initial stages of training, and (ii) to mitigate this effect, we propose a single-step adversarial training method with dropout scheduling.

Autonomous Driving Medical Diagnosis +1

Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes

1 code implementation • CVPR 2020 • Sravanti Addepalli, Vivek B. S., Arya Baburaj, Gaurang Sriramanan, R. Venkatesh Babu

In this work, we attempt to address this problem by training networks to form coarse impressions based on the information in higher bit planes, and use the lower bit planes only to refine their prediction.

Adversarial Robustness

Gray-box Adversarial Training

no code implementations • ECCV 2018 • Vivek B. S., Konda Reddy Mopuri, R. Venkatesh Babu

Adversarial samples are perturbed inputs crafted to mislead the machine learning systems.