Adversarial samples are examples of input data that have been crafted to fool an AI model. They are often used to test the robustness of machine learning algorithms. In this section, we provide an overview of the visualizations related to adversarial samples.
What are Adversarial Samples? Adversarial samples are inputs that are slightly altered from the original data to manipulate the output of a machine learning model. These alterations are often imperceptible to human eyes but can cause significant changes in the model's predictions.
Why Visualize Adversarial Samples? Visualizing adversarial samples can help us understand how AI models make decisions and identify potential vulnerabilities. It can also provide insights into the design of more robust AI systems.
Visualization Techniques
- Heatmaps: Heatmaps can show which parts of an image are most influential in the model's decision-making process.
- LIME (Local Interpretable Model-agnostic Explanations): LIME can explain the decisions of a model by approximating it locally with an interpretable model.
Example Visualization
Further Reading For more information on adversarial samples and their visualizations, you can visit our Adversarial Learning section.