Results 111 to 120 of about 85,609 (269)
Increasing the Robustness of Image Quality Assessment Models Through Adversarial Training
TechnologiesThe adversarial robustness of image quality assessment (IQA) models to adversarial attacks is emerging as a critical issue. Adversarial training has been widely used to improve the robustness of neural networks to adversarial attacks, but little in-depth
Anna Chistyakova +6 more
doaj +1 more source
KNN-guided Adversarial Attacks [PDF]
, 2020 In the last decade, we have witnessed a renaissance of Deep Learning models. Nowadays, they are widely used in industrial as well as scientific fields, and noticeably, these models reached super-human per-formances on specific tasks such as image classification.
Massoli FV, Falchi F, Amato G
openaire +1 more source
Tricking Adversarial Attacks To Fail
CoRR, 2020 Recent adversarial defense approaches have failed. Untargeted gradient-based attacks cause classifiers to choose any wrong class. Our novel white-box defense tricks untargeted attacks into becoming attacks targeted at designated target classes. From these target classes, we can derive the real classes.
openaire +2 more sources
A Systems‐Level Approach to Address Risks and Ethics in Artificial Intelligence Systems
Systems Engineering, EarlyView.ABSTRACT Artificial intelligence (AI) is rapidly changing the world, from completely controlling routine or mundane tasks like text and image generation, to powering advanced algorithms that control critical systems. The recent advances in generative AI quickly overwhelmed multiple industries from education to finance as first adopters rushed (and ...
Vincent P. Paglioni, Torrey Mortenson
wiley +1 more source
Deep Reinforcement Learning-Based Adversarial Attack and Defense in Industrial Control Systems
MathematicsAdversarial attacks targeting industrial control systems, such as the Maroochy wastewater system attack and the Stuxnet worm attack, have caused significant damage to related facilities.
Mun-Suk Kim
doaj +1 more source
Adversarial Attacks on Data Attribution
CoRRAccepted at the 13th International Conference on Learning Representations (ICLR 2025)
Xinhe Wang 0001 +3 more
openaire +3 more sources
World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.ABSTRACT Objective To provide a comprehensive review of the current landscape of artificial intelligence (AI) applications in voice disorder, with emphasis on emerging applications, limitations, and future directions for clinical integration. Methods Literature review.
Rachel B. Kutler, Anaïs Rameau
wiley +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Networked control systems (NCSs) often suffer from performance degradation due to limited communication bandwidth, which can cause data transmission conflicts and packet loss. Existing scheduling strategies may fail to simultaneously meet the real‐time requirements and the importance of multisensor data, and they are particularly vulnerable ...
Da Chen +5 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
DrLS: Distortion‐Resistant Lossless Steganography via Colour Depth Interpolation
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT The lossless data steganography is to hide a certain amount of information into a container image. Previous lossless steganography methods fail to strike a balance between capacity, imperceptibility, accuracy, and robustness, commonly vulnerable to distortion on container images.
Youmin Xu +3 more
wiley +1 more source