23 citations · 38 across the 7 of their papers we have counts for
7 papers
Frequency-Based Vulnerability Analysis of Deep Learning Models against Image Corruptions
Harshitha Machiraju, Michael H. Herzog, Pascal Frossard
Deep learning models often face challenges when handling real-world image corruptions. In response, researchers have developed image corruption datasets to evaluate the performance…
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…
A comment on Guo et al. [arXiv:2206.11228]
Ben Lonnqvist, Harshitha Machiraju, Michael H. Herzog
In a recent article, Guo et al. [arXiv:2206.11228] report that adversarially trained neural representations in deep networks may already be as robust as corresponding primate IT ne…
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…
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…
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.…
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…