5 citations · 5 across the 2 of their papers we have counts for
5 papers
Single-step Adversarial training with Dropout Scheduling
Vivek B. S., R. Venkatesh Babu
Deep learning models have shown impressive performance across a spectrum of computer vision applications including medical diagnosis and autonomous driving. One of the major concer…
Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes
Sravanti Addepalli, Vivek B. S., Arya Baburaj +2
As humans, we inherently perceive images based on their predominant features, and ignore noise embedded within lower bit planes. On the contrary, Deep Neural Networks are known to…
Regularizers for Single-step Adversarial Training
B. S. Vivek, R. Venkatesh Babu
The progress in the last decade has enabled machine learning models to achieve impressive performance across a wide range of tasks in Computer Vision. However, a plethora of works…
FDA: Feature Disruptive Attack
Aditya Ganeshan, B. S. Vivek, R. Venkatesh Babu
Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i.e., image sample…
Gray-box Adversarial Training
Vivek B. S., Konda Reddy Mopuri, R. Venkatesh Babu
Adversarial samples are perturbed inputs crafted to mislead the machine learning systems. A training mechanism, called adversarial training, which presents adversarial samples alon…