33 citations · 44 across the 6 of their papers we have counts for
15 papers · 1 filter
DNN-Compressed Domain Visual Recognition with Feature Adaptation
Yingpeng Deng, Lina J. Karam
Learning-based image compression was shown to achieve a competitive performance with state-of-the-art transform-based codecs. This motivated the development of new learning-based v…
Learning-based Compression for Material and Texture Recognition
Yingpeng Deng, Lina J. Karam
Learning-based image compression was shown to achieve a competitive performance with state-of-the-art transform-based codecs. This motivated the development of new learning-based v…
Towards Imperceptible Universal Attacks on Texture Recognition
Yingpeng Deng, Lina J. Karam
Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks o…
A Study for Universal Adversarial Attacks on Texture Recognition
Yingpeng Deng, Lina J. Karam
Given the outstanding progress that convolutional neural networks (CNNs) have made on natural image classification and object recognition problems, it is shown that deep learning m…
Frequency-Tuned Universal Adversarial Attacks
Yingpeng Deng, Lina J. Karam
Researchers have shown that the predictions of a convolutional neural network (CNN) for an image set can be severely distorted by one single image-agnostic perturbation, or univers…
Defending Against Universal Attacks Through Selective Feature Regeneration
Tejas Borkar, Felix Heide, Lina Karam
Deep neural network (DNN) predictions have been shown to be vulnerable to carefully crafted adversarial perturbations. Specifically, image-agnostic (universal adversarial) perturba…