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Found 9 papers, 9 papers with code
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples
Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i. e. images whose content cannot be used to improve a classifier during training.
On Success and Simplicity: A Second Look at Transferable Targeted Attacks
In particular, we, for the first time, identify that a simple logit loss can yield competitive results with the state of the arts.
Adversarial Robustness Against Image Color Transformation within Parametric Filter Space
In this paper, we carry out a systematic robustness analysis of ACE from both the attack and defense perspectives by varying the bound of the color filter parameters.
Profile Consistency Identification for Open-domain Dialogue Agents
Maintaining a consistent attribute profile is crucial for dialogue agents to naturally converse with humans.
Adversarial Color Enhancement: Generating Unrestricted Adversarial Images by Optimizing a Color Filter
We introduce an approach that enhances images using a color filter in order to create adversarial effects, which fool neural networks into misclassification.
Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance
The success of image perturbations that are designed to fool image classifier is assessed in terms of both adversarial effect and visual imperceptibility.
Who's Afraid of Adversarial Queries? The Impact of Image Modifications on Content-based Image Retrieval
An adversarial query is an image that has been modified to disrupt content-based image retrieval (CBIR) while appearing nearly untouched to the human eye.
From Volcano to Toyshop: Adaptive Discriminative Region Discovery for Scene Recognition
As deep learning approaches to scene recognition emerge, they have continued to leverage discriminative regions at multiple scales, building on practices established by conventional image classification research.
