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Found 6 papers, 2 papers with code
CGBA: Curvature-aware Geometric Black-box Attack
While the proposed CGBA attack can work effectively for an arbitrary decision boundary, it is particularly efficient in exploiting the low curvature to craft high-quality adversarial examples, which is widely seen and experimentally verified in commonly used classifiers under non-targeted attacks.
BERT-DRE: BERT with Deep Recursive Encoder for Natural Language Sentence Matching
Three Bi-LSTM layers with residual connection are used to design a recursive encoder and an attention module is used on top of this encoder.
On the exploitative behavior of adversarial training against adversarial attacks
Adversarial attacks have been developed as intentionally designed perturbations added to the inputs in order to fool deep neural network classifiers.
Adversarial training may be a double-edged sword
Adversarial training has been shown as an effective approach to improve the robustness of image classifiers against white-box attacks.
Lifetime Maximization for UAV-assisted Data Gathering Networks in the Presence of Jamming
Deployment of unmanned aerial vehicles (UAVs) is recently getting significant attention due to a variety of practical use cases, such as surveillance, data gathering, and commodity delivery.
GeoDA: a geometric framework for black-box adversarial attacks
We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-$1$ label of the classifier.
