Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box... Adversarial Reprogramming Revisited DISCO: Adversarial Defense with Local Implicit Functions Synergy-of-Experts: Collaborate to Improve Adversarial Robustness Adversarial Unlearning: Reducing Confidence Along Adversarial...这篇是跟对抗相关,...
The code is the official implementation of NeurIPS paper Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks This repository supports data protection on CIFAR-10 and ImageNet The experiments are run in an NVIDIA A100 GPU, but could modify the batch size ...
The only precaution might be to limit the amount of gradient based information provided or encode the gradient information in a way that is provides a comprehension of the model as well as making it difficult for attackers to backtrack and work out the details of the gradient value itself....
In the race of arms between attackers, trying to build more and more realistic face replay attacks, and defenders, deploying spoof detection modules with ever-increasing capabilities, CNN-based methods have shown outstanding detection performance thus raising the bar for the construction of realistic ...
adversarial attackweb seDetecting malicious Uniform Resource Locators(URLs)is crucially important to prevent attackers from committing cybercrimes.Recent researches have investigated the role of machine learning(ML)models to detect malicious URLs.By using ML algorithms,rst,the features of URLs are ...
The critical intelligence you need to know about your attack surface and exposure — you’ll see what the attackers see. Context– Attacks continue to increase, with more threats than ever, but most lack context that could aid in determining risky behavior or material risk to the information en...
The challenge with this approach is that machine learning itself comes with vulnerabilities – and if left unattended presents a new attack surface for attackers to exploit. In this paper we present a survey of research in the area of machine learning-based malware classifiers, the attacks they ...
Attackers often do not care about the entire model, but only some specific information, like a secret password. Inference attacks focus on the data used to train the model. The goal is to extract confidential data from the model. Through carefully crafted queries, this information can be releas...
Adversarial attacks have shown significant potential to be feasible even for state-of-the-art DNNs, regardless of the attackers amount of accessibility to the model and being perceptible to human eye. Compared to other domains of computer vision, medical DNNs are very fragile against adversarial att...
A Decision Framework for Managing Risk to Airports from Terrorist Attack models the security system of an asset, considers various threat scenarios, and models the sequential decision framework of attackers during the attack. Its... A Shafieezadeh,EJ Cha,BR Ellingwood - 《Risk Analysis》 被引量:...