This page provides the attacks and factors that have a negative impact “Difficulty in understanding AI inference results” 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
- Integrity violation
- Attacks against explainability
Defensive method or countermeasure
Targeted AI technology
- DNN
- CNN
- Contrastive learning
- FSL
- GNN
- Federated learning
- LSTM
- RNN
Task
- Classification
Data
- Image
- Graph
- Text
- Audio
Related external influence aspect
References
Attacks against explainability
XAI (Explainable AI)
- Visualizing and Understanding Convolutional Networks, 2014
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014
- Understanding Deep Image Representations by Inverting Them, 2014
- “Why Should I Trust You?”: Explaining the Predictions of Any Classifier, 2016
- A Unified Approach to Interpreting Model Predictions, 2017
- Learning Important Features Through Propagating Activation Differences, 2017
- Understanding Black-box Predictions via Influence Functions, 2017
- Interpretable Explanations of Black Boxes by Meaningful Perturbation, 2017
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), 2018
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, 2019