activity
20192022
most citedHarnessing the Vulnerability of Latent Layers in Adversarially Trained Models

23 citations · 25 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SD2022

The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection

Daniele Mari, Federica Latora, Simone Milani

The recent integration of generative neural strategies and audio processing techniques have fostered the widespread of synthetic speech synthesis or transformation algorithms. This…

cs.CV20221 cited

Empirical Advocacy of Bio-inspired Models for Robust Image Recognition

Harshitha Machiraju, Oh-Hyeon Choung, Michael H. Herzog +1

Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many…

cs.CV20211 cited

Bio-inspired Robustness: A Review

Harshitha Machiraju, Oh-Hyeon Choung, Pascal Frossard +1

Deep convolutional neural networks (DCNNs) have revolutionized computer vision and are often advocated as good models of the human visual system. However, there are currently many…

cs.LG2020

A Little Fog for a Large Turn

Harshitha Machiraju, Vineeth N Balasubramanian

Small, carefully crafted perturbations called adversarial perturbations can easily fool neural networks. However, these perturbations are largely additive and not naturally found.…

cs.LG201923 cited

Harnessing the Vulnerability of Latent Layers in Adversarially Trained Models

Mayank Singh, Abhishek Sinha, Nupur Kumari +3

Neural networks are vulnerable to adversarial attacks -- small visually imperceptible crafted noise which when added to the input drastically changes the output. The most effective…