Шабуыл сәттілік деңгейі
Related Papers
Word-level Textual Adversarial Attacking as Combinatorial Optimization
AbstractAdversarial attacks are carried out to reveal the vulnerability of deep neural networks. Textual adversarial attacking is challenging because text is discrete and a small perturbation can bring significant change …...
Contextualized Perturbation for Textual Adversarial Attack
AbstractAdversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of generating such examples are typically driven …...
TABS: Efficient Textual Adversarial Attack for Pre-trained NL Code Model Using Semantic Beam Search
AbstractAs pre-trained models have shown successful performance in program language processing as well as natural language processing, adversarial attacks on these models also attract attention. However, previous works on black-box …...
ValCAT: Variable-Length Contextualized Adversarial Transformations Using Encoder-Decoder Language Model
AbstractAdversarial texts help explore vulnerabilities in language models, improve model robustness, and explain their working mechanisms. However, existing word-level attack methods trap in a one-to-one attack pattern, i.e., only a …...
NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models
AbstractPrompt-based learning is vulnerable to backdoor attacks. Existing backdoor attacks against prompt-based models consider injecting backdoors into the entire embedding layers or word embedding vectors. Such attacks can be easily …...