The Path to Defence: A Roadmap to Characterising Data Poisoning Attacks on Victim Models
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Title
The Path to Defence: A Roadmap to Characterising Data Poisoning Attacks on Victim Models
Authors
Keywords
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Journal
ACM COMPUTING SURVEYS
Volume -, Issue -, Pages -
Publisher
Association for Computing Machinery (ACM)
Online
2023-10-28
DOI
10.1145/3627536
References
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Related references
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- (2021) Jingjing Li et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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- (2020) Yucheng Shi et al. PATTERN RECOGNITION
- Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces
- (2020) Santiago Paternain et al. IEEE TRANSACTIONS ON AUTOMATIC CONTROL
- A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop
- (2019) Curtis P. Langlotz et al. RADIOLOGY
- One Pixel Attack for Fooling Deep Neural Networks
- (2019) Jiawei Su et al. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
- Hiding data in dual color images reversibly via weighted matrix
- (2019) Partha Chowdhuri et al. Journal of Information Security and Applications
- Adversarial image detection in deep neural networks
- (2018) Fabio Carrara et al. MULTIMEDIA TOOLS AND APPLICATIONS
- Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- (2018) Naveed Akhtar et al. IEEE Access
- Efficient agglomerative hierarchical clustering
- (2015) Athman Bouguettaya et al. EXPERT SYSTEMS WITH APPLICATIONS
- A natural gradient descent algorithm for the solution of discrete algebraic Lyapunov equations based on the geodesic distance
- (2013) Xiaomin Duan et al. APPLIED MATHEMATICS AND COMPUTATION
- An SVM-based machine learning method for accurate internet traffic classification
- (2008) Ruixi Yuan et al. INFORMATION SYSTEMS FRONTIERS
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