A survey on transferability of adversarial examples across deep neural networks
The emergence of Deep Neural Networks (DNNs) has revolutionized various domains,
enabling the resolution of complex tasks spanning image recognition, natural language …
enabling the resolution of complex tasks spanning image recognition, natural language …
Comparative evaluation of recent universal adversarial perturbations in image classification
Abstract The vulnerability of Convolutional Neural Networks (CNNs) to adversarial samples
has recently garnered significant attention in the machine learning community. Furthermore …
has recently garnered significant attention in the machine learning community. Furthermore …
Learning transferable targeted universal adversarial perturbations by sequential meta-learning
Recently, the transferability of adversarial perturbations in non-targeted scenarios has been
extensively studied. However, changing the predictions of an unknown model to a pre …
extensively studied. However, changing the predictions of an unknown model to a pre …
LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack
Natural language processing models are vulnerable to adversarial examples. Previous
textual adversarial attacks adopt model internal information (gradients or confidence scores) …
textual adversarial attacks adopt model internal information (gradients or confidence scores) …
Beamattack: Generating high-quality textual adversarial examples through beam search and mixed semantic spaces
H Zhu, Q Zhao, Y Wu - Pacific-Asia Conference on Knowledge Discovery …, 2023 - Springer
Natural language processing models based on neural networks are vulnerable to
adversarial examples. These adversarial examples are imperceptible to human readers but …
adversarial examples. These adversarial examples are imperceptible to human readers but …
Non-cooperative game theory with generative adversarial network for effective decision-making in military cyber warfare
X Ma, W Abdelfattah, D Luo, N Innab… - Annals of Operations …, 2024 - Springer
Cyber warfare has become a critical domain of modern military operations, characterized by
the constant interplay of offense and defense in the digital realm. This research explores a …
the constant interplay of offense and defense in the digital realm. This research explores a …
BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries
Machine learning security has recently become a prominent topic in the natural language
processing (NLP) area. The existing black-box adversarial attack suffers prohibitively from …
processing (NLP) area. The existing black-box adversarial attack suffers prohibitively from …
MAT: mixed-strategy game of adversarial training in fine-tuning
Fine-tuning large-scale pre-trained language models has been demonstrated effective for
various natural language processing (NLP) tasks. Previous studies have established that …
various natural language processing (NLP) tasks. Previous studies have established that …
DeepMC: DNN test sample optimization method jointly guided by misclassification and coverage
J Sun, J Li, S Wen - Applied Intelligence, 2023 - Springer
Large-scale and high-quality test samples are extremely scarce in deep neural networks
(DNN) testing. Existing test sample optimization methods exhibit the problem of low …
(DNN) testing. Existing test sample optimization methods exhibit the problem of low …
A Framework to Enhance Security and Safety of Deep Learning Models Against Out-of-Distribution Examples
A Azmoodeh - 2024 - atrium.lib.uoguelph.ca
In recent years, the realm of deep learning has witnessed remarkable progress, with models
rooted in this paradigm surpassing traditional algorithms and, in some instances, human …
rooted in this paradigm surpassing traditional algorithms and, in some instances, human …