Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
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Summary
This paper presents the first comprehensive survey on adversarial attacks on deep learning in computer vision, reviewing the works that design adversarial attack, analyze the existence of such attacks and propose defenses against them.
- Type
- preprint
- Published
- 2018-01-02
- Cited by
- 2,092
- References
- 205
- Access
- Open access
- OpenAlex
- https://openalex.org/W2782217514
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3536399
Keywords
Adversarial system, Deep learning, Artificial intelligence, Computer science, Adversarial machine learning
References
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- Towards Deep Neural Network Architectures Robust to Adversarial Examples
- Fully convolutional networks for semantic segmentation
- Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
- MatConvNet: Convolutional Neural Networks for MATLAB
- Online particle detection with Neural Networks based on topological calorimetry information
- Return of the Devil in the Details: Delving Deep into Convolutional Nets
- Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships
- Long Short-Term Memory
- Learning Deep Architectures for AI
- Practical Methods of Optimization: Fletcher/Practical Methods of Optimization
- The human splicing code reveals new insights into the genetic determinants of disease
- Going deeper with convolutions
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- Deep packet: a novel approach for encrypted traffic classification using deep learning
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
- High Dimensional Spaces, Deep Learning and Adversarial Examples
- Security and Privacy Approaches in Mixed Reality
- Robustness of Rotation-Equivariant Networks to Adversarial Perturbations
- Detecting Adversarial Examples via Neural Fingerprinting
- On Generation of Adversarial Examples using Convex Programming
- Defending against Adversarial Images using Basis Functions Transformations
- The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples
- ADef: an Iterative Algorithm to Construct Adversarial Deformations
- Fast Neural Network Training on FPGA Using Quasi-Newton Optimization Method
- Training verified learners with learned verifiers
- Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference
- Bidirectional Learning for Robust Neural Networks
- Adversarial Attacks on Face Detectors Using Neural Net Based Constrained Optimization
- Hardware Trojan Attacks on Neural Networks
- Basic functional trade-offs in cognition: An integrative framework.
- Local Gradients Smoothing: Defense Against Localized Adversarial Attacks
- A general metric for identifying adversarial images
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