Adversarial patches - Open Access .click

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POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI

IEEE Access
Adversarial examples, which are carefully crafted inputs designed to deceive deep learning models, create significant challenges in Artificial Intelligence.
Han-Ju Lee +3 more
doaj +1 more source

Dual-Branch Spectral–Spatial Adversarial Representation Learning for Hyperspectral Image Classification With Few Labeled Samples

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Recently, deep learning methods, particularly the convolutional neural networks, have been extensively employed for extracting spectral–spatial features in hyperspectral image (HSI) classification tasks, yielding promising results.
Caihao Sun +5 more
doaj +1 more source

Unsupervised Holistic Image Generation from Key Local Patches

, 2017
We introduce a new problem of generating an image based on a small number of key local patches without any geometric prior. In this work, key local patches are defined as informative regions of the target object or scene.
C Li +9 more
core +1 more source

Domain-adversarial neural networks to address the appearance variability of histopathology images

, 2017
Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance ...
Eppenhof, Koen A. J. +4 more
core +1 more source

URAdv: A Novel Framework for Generating Ultra-Robust Adversarial Patches Against UAV Object Detection

Mathematics
In recent years, deep learning has been extensively deployed on unmanned aerial vehicles (UAVs), particularly for object detection. As the cornerstone of UAV-based object detection, deep neural networks are susceptible to adversarial attacks, with ...
Hailong Xi +6 more
doaj +1 more source

TextureGAN: Controlling Deep Image Synthesis with Texture Patches

, 2018
In this paper, we investigate deep image synthesis guided by sketch, color, and texture. Previous image synthesis methods can be controlled by sketch and color strokes but we are the first to examine texture control.
Agrawal, Varun +7 more
core +1 more source

Distributional Modeling for Location-Aware Adversarial Patches

CoRR, 2023
Adversarial patch is one of the important forms of performing adversarial attacks in the physical world. To improve the naturalness and aggressiveness of existing adversarial patches, location-aware patches are proposed, where the patch's location on the target object is integrated into the optimization process to perform attacks.
Xingxing Wei 0001 +3 more
openaire +2 more sources

Learning Segmentation Masks with the Independence Prior

, 2018
An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images.
Chen, Yimin +5 more
core +1 more source

Adversarial Inpainting of Medical Image Modalities

, 2018
Numerous factors could lead to partial deteriorations of medical images. For example, metallic implants will lead to localized perturbations in MRI scans.
Armanious, Karim +3 more
core +1 more source

Learnable Diffusion Framework for Mouse V1 Neural Decoding

Advanced Science, EarlyView.
We introduce Sensorium‐Viz, a diffusion‐based framework for reconstructing high‐fidelity visual stimuli from mouse primary visual cortex activity. By integrating a novel spatial embedding module with a Diffusion Transformer (DiT) and a synthetic‐response augmentation strategy, our model outperforms state‐of‐the‐art fMRI‐based baselines, enabling robust
Kaiwen Deng, Peter S. Schwendeman, Yuanfang Guan +2 more
wiley +1 more source

adversarial patch
adversarial attack
object detection

deep learning
cnn
adversarial attacks

adversarial robustness
deep neural networks