Adversarial robustness improvement for deep neural networks
Sign Up to like & getrecommendations! Published in 2024 at "Machine Vision and Applications"
DOI: 10.1007/s00138-024-01519-1
Abstract: Deep neural networks (DNNs) are key components for the implementation of autonomy in systems that operate in highly complex and unpredictable environments (self-driving cars, smart traffic systems, smart manufacturing, etc.). It is well known that… read more here.
Keywords: robustness improvement; robustness; deep neural; neural networks ... See more keywords
Exploring biologically inspired mechanisms of adversarial robustness
Sign Up to like & getrecommendations! Published in 2024 at "Neural Computing and Applications"
DOI: 10.1007/s00521-025-11019-6
Abstract: Backpropagation-optimized artificial neural networks, while precise, lack robustness, leading to unforeseen behaviors that affect their safety. Biological neural systems do solve some of these issues already. Unlike artificial models, biological neurons adjust connectivity based on… read more here.
Keywords: biologically inspired; robustness; power law; inspired mechanisms ... See more keywords
Ensemble of Random Binary Output Encoding for Adversarial Robustness
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2937604
Abstract: Despite the excellent classification performance, recent research has revealed that the Convolutional Neural Network (CNN) could be readily deceived by only the small adversarial perturbation. Its imperceptible to human eyes and transferability from one model… read more here.
Keywords: robustness; ensemble random; adversarial robustness; binary output ... See more keywords
Evaluating Adversarial Robustness of Secret Key-Based Defenses
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3162874
Abstract: The vulnerability of neural networks to adversarial attacks has inspired the proposal of many defenses. Key-based input transformation techniques are the recently proposed methods that make use of gradient obfuscation to improve the adversarial robustness… read more here.
Keywords: key based; based defenses; adversarial robustness; transformation ... See more keywords
RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3214312
Abstract: Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network (DNN) architectures for a given application under given system constraints. DNNs are computationally-complex as well as vulnerable to adversarial attacks. In order to… read more here.
Keywords: hardware; robustness hardware; hardware efficiency; adversarial robustness ... See more keywords
Improving Adversarial Robustness With Adversarial Augmentations
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2023.3301608
Abstract: Deep neural network (DNN)-based applications are extensively being researched and applied in the Internet of Things (IoT) devices in daily lives due to impressive performance. Recently, adversarial attacks pose a significant threat to the security… read more here.
Keywords: self supervised; training; adversarial training; adversarial attacks ... See more keywords
UniQGAN: Towards Improved Modulation Classification With Adversarial Robustness Using Scalable Generator Design
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2023.3261983
Abstract: Automatic modulation classification (AMC) has been envisioned as a significant element for security issues at the physical layer due to its indispensable role in accurate communications. Recent attention to deep learning has impacted the AMC,… read more here.
Keywords: classification; training; modulation classification; modulation ... See more keywords
Unrevealed Threats: Adversarial Robustness Analysis of Underwater Image Enhancement Models
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2025.3613152
Abstract: Learning-based methods for underwater image enhancement (UWIE) have undergone extensive exploration. However, learning-based models are usually vulnerable to adversarial examples so as the UWIE models. To the best of our knowledge, there is no comprehensive… read more here.
Keywords: underwater image; uwie models; adversarial attacks; image enhancement ... See more keywords
On the Adversarial Robustness of Learning-based Image Compression Against Rate-Distortion Attacks
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Multimedia"
DOI: 10.1109/tmm.2025.3639935
Abstract: Despite demonstrating superior rate-distortion (RD) performance, learning-based image compression (LIC) algorithms have been found to be vulnerable to malicious perturbations in recent studies. However, the adversarial attacks considered in existing literature remain divergent from real-world… read more here.
Keywords: rate; rate distortion; compression; adversarial robustness ... See more keywords
Adversarial robustness assessment: Why in evaluation both L0 and L∞ attacks are necessary
Sign Up to like & getrecommendations! Published in 2022 at "PLoS ONE"
DOI: 10.1371/journal.pone.0265723
Abstract: There are different types of adversarial attacks and defences for machine learning algorithms which makes assessing the robustness of an algorithm a daunting task. Moreover, there is an intrinsic bias in these adversarial attacks and… read more here.
Keywords: adversarial robustness; robustness; robustness assessment; assessment evaluation ... See more keywords