Results 61 to 70 of about 31,109 (263)
Deep Generative Adversarial Compression Artifact Removal
, 2017 Compression artifacts arise in images whenever a lossy compression algorithm is applied. These artifacts eliminate details present in the original image, or add noise and small structures; because of these effects they make images less pleasant for the ...
Bertini, Marco +3 more
core +1 more source
DPatch: An Adversarial Patch Attack on Object Detectors [PDF]
, 2018 Object detectors have emerged as an indispensable module in modern computer vision systems. In this work, we propose DPatch -- a black-box adversarial-patch-based attack towards mainstream object detectors (i.e. Faster R-CNN and YOLO). Unlike the original adversarial patch that only manipulates image-level classifier, our DPatch simultaneously attacks ...
Xin Liu 0075 +5 more
openaire +2 more sources
Detecting Patch Adversarial Attacks with Image Residuals
CoRR, 2020 We introduce an adversarial sample detection algorithm based on image residuals, specifically designed to guard against patch-based attacks. The image residual is obtained as the difference between an input image and a denoised version of it, and a discriminator is trained to distinguish between clean and adversarial samples.
Marius Arvinte +2 more
openaire +2 more sources
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 +2 more
wiley +1 more source
Remote SensingVision Transformers (ViTs) have demonstrated strong performance in hyperspectral image (HSI) classification; however, their robustness is highly sensitive to patch size.
Shashi Kiran Chandrappa +2 more
doaj +1 more source
PatchZero: Defending against Adversarial Patch Attacks by Detecting and Zeroing the Patch
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023 Accepted to WACV ...
Ke Xu +4 more
openaire +2 more sources
Advanced Science, EarlyView.This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo +6 more
wiley +1 more source
POSES: Patch Optimization Strategies for Efficiency and Stealthiness Using eXplainable AI
IEEE AccessAdversarial 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
Perceptual-Sensitive GAN for Generating Adversarial Patches
Proceedings of the AAAI Conference on Artificial Intelligence, 2019 Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world.
Aishan Liu +6 more
openaire +2 more sources
Jedi: Entropy-Based Localization and Removal of Adversarial Patches
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Real-world adversarial physical patches were shown to be successful in compromising state-of-the-art models in a variety of computer vision applications. Existing defenses that are based on either input gradient or features analysis have been compromised by recent GAN-based attacks that generate naturalistic patches.
Tarchoun, Bilel +4 more
openaire +4 more sources