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Deep neural networks (DNNs) are vulnerable to adversarial examples, which can mislead models without affecting normal judgment of humans. In the image field, such adversarial examples involve small perturbations that humans rarely notice. However, ...

Although natural language processing technology has shown strong performance in many tasks, it is very vulnerable to adversarial examples, i.e., sentences with some small perturbations can fool AI models. Current adversarial texts for English are ...

It is known that neural networks are subject to attacks through adversarial perturbations. Worse yet, such attacks are impossible to eliminate, i.e., the adversarial perturbation is still possible after applying mitigation methods such as ...

Protecting the user's personality privacy can effectively interfere with or deceive the attacker's personality analysis, avoid the attacker's use of personality vulnerability, and reduce the success rate of social engineering attacks. However, the ...

Deep learning (DL) technology has been widely deployed in many fields and achieved great success, but it is not absolutely safe and reliable. It has been proved that research on adversarial attacks can reveal the vulnerability of deep neural ...

Adversarial examples have received increasing attention recently due to their significant values in evaluating and improving the robustness of deep neural networks. Existing adversarial attack algorithms have achieved good result for most images. ...