This page provides the attacks and factors that have a negative impact “Manipulation of inference results by deliberate noise” in the information systems aspect in the AI Security Map, the defense methods and countermeasures against them, as well as the relevant AI technologies, tasks, and data. It also indicates related elements in the external influence aspect.
Attack or cause
- Adversarial examples
- Adversarial training
Defensive method or countermeasure
- Detection of adversarial examples
- Certified robustness
- Model safety evaluation
Targeted AI technology
- DNN
- CNN
- LLM
- Contrastive learning
- FSL
- GNN
- Federated learning
- LSTM
- RNN
Task
- Classification
Data
- Image
- Graph
- Text
- Audio
Related external influence aspect
References
Adversarial examples
- Intriguing properties of neural networks, 2014
- Explaining and Harnessing Adversarial Examples, 2015
- The limitations of deep learning in adversarial settings, 2015
- Adversarial Examples in the Physical World, 2017
- Towards Evaluating the Robustness of Neural Networks, 2017
- Towards Deep Learning Models Resistant to Adversarial Attacks, 2018
- A Closer Look at Deep Learning Heuristics: Learning Rate Restarts, Warmup and Decay, 2020
Certified robustness
- Certified Defenses for Data Poisoning Attacks, 2017
- Certified Robustness to Adversarial Examples with Differential Privacy, 2019
- On Evaluating Adversarial Robustness, 2019
- Certified Adversarial Robustness via Randomized Smoothing, 2019
- Certified Robustness of Graph Neural Networks against Adversarial Structural Perturbation, 2021
- Certified Robustness for Large Language Models with Self-Denoising, 2023
- RAB: Provable Robustness Against Backdoor Attacks, 2023
- (Certified!!) Adversarial Robustness for Free!, 2023
- Certifying LLM Safety against Adversarial Prompting, 2024