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An interpretable model based on concept and argumentation for tabular data. [PDF]
Sci RepChi H, Wang D, Liao B, Cui G, Mao F.
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Reverse Attack: Black-box Attacks on Collaborative Recommendation
Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021Collaborative filtering (CF) recommender systems have been extensively developed and widely deployed in various social websites, promoting products or services to the users of interest. Meanwhile, work has been attempted at poisoning attacks to CF recommender systems for distorting the recommend results to reap commercial or personal gains stealthily ...
Yihe Zhang 0001 +5 more
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Substitute Meta-Learning for Black-Box Adversarial Attack
IEEE Signal Processing Letters, 2022Cong Hu, Xiao-Jun Wu
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Black-Box Data Poisoning Attacks on Crowdsourcing
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023Understanding the vulnerability of label aggregation against data poisoning attacks is key to ensuring data quality in crowdsourced label collection. State-of-the-art attack mechanisms generally assume full knowledge of the aggregation models while failing to consider the flexibility of malicious workers in selecting which instances to label.
Pengpeng Chen +5 more
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Black-box adversarial attacks on XSS attack detection model
Computers & Security, 2022Abstract Cross-site scripting (XSS) has been extensively studied, although mitigating such attacks in web applications remains challenging. While there is an increasing number of XSS attack detection approaches designed based on machine learning and deep learning algorithms, it is important to study and evaluate the reliability and security of these ...
Qiuhua Wang +6 more
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Black Box Attacks on Deep Anomaly Detectors
Proceedings of the 14th International Conference on Availability, Reliability and Security, 2019The process of identifying the true anomalies from a given set of data instances is known as anomaly detection. It has been applied to address a diverse set of problems in multiple application domains including cybersecurity. Deep learning has recently demonstrated state-of-the-art performance on key anomaly detection applications, such as intrusion ...
Aditya Kuppa +3 more
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Common Component in Black-Boxes Is Prone to Attacks
2021Neural network models are getting increasingly complex. Large models are often modular, consisting of multiple separate sharable components. The development of such components may require specific domain knowledge, intensive computation power, and large datasets.
Jiyi Zhang +3 more
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Simulator Attack+ for Black-Box Adversarial Attack
2022 IEEE International Conference on Image Processing (ICIP), 2022Yimu Ji 0001 +7 more
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Schmidt: Image Augmentation for Black-Box Adversarial Attack
2018 IEEE International Conference on Multimedia and Expo (ICME), 2018Despite achieving great success in multimedia analysis, especially in image recognition, deep neural networks (DNNs) can be easily fooled by maliciously crafted adversarial examples. Attacker who generates adversarial examples can even launch black-box adversarial attack by querying the target DNN model, without access to its internal structure or ...
Yucheng Shi, Yahong Han
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