Results 11 to 20 of about 26,718 (222)

Ally patches for spoliation of adversarial patches [PDF]

Journal of Big Data, 2019
Adversarial attacks represent a serious evolving threat to the operation of deep neural networks. Recently, adversarial algorithms were developed to facilitate hallucination of deep neural networks for ordinary attackers.
Alaa E. Abdel-Hakim
doaj   +2 more sources

IPatch: a remote adversarial patch

Cybersecurity, 2023
Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame. In the past, it has been shown how an attacker can fool these models by placing an adversarial patch ...
Yisroel Mirsky
doaj   +3 more sources

Benchmarking Adversarial Patch Selection and Location

Mathematics
Adversarial patch attacks threaten the reliability of modern vision models. We present PatchMap, the first spatially exhaustive benchmark of patch placement, built by evaluating over 1.5×108 forward passes on ImageNet validation images.
Shai Kimhi, Moshe Kimhi, Avi Mendelson
doaj   +3 more sources

Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches

CoRR, 2021
9 pages, 7 ...
Nan Ji, YanFei Feng, Haidong Xie, Xueshuang Xiang, Naijin Liu   +4 more
openaire   +2 more sources

TPatch: A Triggered Physical Adversarial Patch

CoRR, 2023
Appeared in 32nd USENIX Security Symposium (USENIX Security 23)
Wenjun Zhu, Xiaoyu Ji 0001, Yushi Cheng, Shibo Zhang, Wenyuan Xu 0001   +4 more
openaire   +3 more sources

Adversarial Training Against Location-Optimized Adversarial Patches [PDF]

, 2020
20 pages, 6 tables, 4 figures, 2 algorithms, European Conference on Computer Vision Workshops ...
Sukrut Rao, David Stutz, Bernt Schiele
openaire   +4 more sources

Certified Defenses for Adversarial Patches

CoRR, 2020
International Conference on Learning Representations, ICLR ...
Chiang, Ping-yeh, Ni, Renkun, Abdelkader, Ahmed, Zhu, Chen, Studer, Christoph; id_orcid0000-0001-8950-6267, Goldstein, Tom   +5 more
openaire   +4 more sources

Generating Visually Realistic Adversarial Patch

CoRR, 2023
Deep neural networks (DNNs) are vulnerable to various types of adversarial examples, bringing huge threats to security-critical applications. Among these, adversarial patches have drawn increasing attention due to their good applicability to fool DNNs in the physical world.
Xiaosen Wang, Kunyu Wang
openaire   +2 more sources

Adversarial patch camouflage against aerial detection [PDF]

Artificial Intelligence and Machine Learning in Defense Applications II, 2020
Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. The traditional way of hiding military assets from sight is camouflage, for example by using camouflage nets. However, large assets like planes or vessels are difficult to conceal by means of traditional camouflage
Hollander, R.J.M. den, Adhikari,A., Tolios, I., Bekkum, M. van, Bal, A., Hendriks, S., Kruithof, M.C., Gross, D., Jansen, N., Perez, G., Buurman, K., Raaijmakers, S.A.   +11 more
openaire   +4 more sources

Scale-Adaptive Adversarial Patch Attack for Remote Sensing Image Aircraft Detection

Remote Sensing, 2021
With the adversarial attack of convolutional neural networks (CNNs), we are able to generate adversarial patches to make an aircraft undetectable by object detectors instead of covering the aircraft with large camouflage nets. However, aircraft in remote
Mingming Lu, Qi Li, Li Chen, Haifeng Li
doaj   +1 more source