Results 31 to 40 of about 5,380,268 (331)
Mist: Towards Improved Adversarial Examples for Diffusion Models [PDF]
arXiv.org, 2023 Diffusion Models (DMs) have empowered great success in artificial-intelligence-generated content, especially in artwork creation, yet raising new concerns in intellectual properties and copyright. For example, infringers can make profits by imitating non-
Chumeng Liang, Xiaoyu Wu
semanticscholar +1 more source
Enhancing Adversarial Defense via Brain Activity Integration Without Adversarial Examples. [PDF]
Sensors (Basel)Adversarial attacks on large-scale vision–language foundation models, such as the contrastive language–image pretraining (CLIP) model, can significantly degrade performance across various tasks by generating adversarial examples that are ...
Nakajima T +4 more
europepmc +2 more sources
Journal of Japan Society for Fuzzy Theory and Intelligent InformaticsYuki Shinomiya
semanticscholar +2 more sources
Survey of Image Adversarial Example Defense Techniques [PDF]
Jisuanji kexue yu tansuo, 2023 The rapid and extensive growth of artificial intelligence introduces new security challenges. The generation and defense of adversarial examples for deep neural networks is one of the hot spots.
LIU Ruiqi, LI Hu, WANG Dongxia, ZHAO Chongyang, LI Boyu
doaj +1 more source
Distinguishability of adversarial examples [PDF]
Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020 Machine learning models can be easily fooled by adversarial examples which are generated from clean examples with small perturbations. This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many important domains such as computer vision and malware detection. From a unique angle, we propose
Yi Qin, Ryan Hunt, Chuan Yue
openaire +1 more source
Fooling Examples: Another Intriguing Property of Neural Networks
Sensors, 2023 Neural networks have been proven to be vulnerable to adversarial examples; these are examples that can be recognized by both humans and neural networks, although neural networks give incorrect predictions.
Ming Zhang, Yongkang Chen, Cheng Qian
doaj +1 more source
LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples [PDF]
arXiv.org, 2023 Large Language Models (LLMs), including GPT-3.5, LLaMA, and PaLM, seem to be knowledgeable and able to adapt to many tasks. However, we still cannot completely trust their answers, since LLMs suffer from \textbf{hallucination}\textemdash fabricating non ...
Jia-Yu Yao +4 more
semanticscholar +1 more source
Verifying the Causes of Adversarial Examples [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs, which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial ...
Li, H +4 more
openaire +3 more sources
Improving Transferability of Adversarial Examples With Input Diversity [PDF]
Computer Vision and Pattern Recognition, 2018 Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples --- crafted by adding human-imperceptible perturbations to clean images.
Cihang Xie +5 more
semanticscholar +1 more source
Efficient Adversarial Training With Transferable Adversarial Examples [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Adversarial training is an effective defense method to protect classification models against adversarial attacks. However, one limitation of this approach is that it can require orders of magnitude additional training time due to high cost of generating strong adversarial examples during training.
Haizhong Zheng +4 more
openaire +2 more sources