The document discusses methods of detecting adversarial inputs in AI and machine learning through image processing techniques. It outlines various types of adversarial attacks, including poisoning, evasion, and Trojan AI, and emphasizes the quantifiable noise differences between clean and adversarial images. The methodology proposed aims to effectively identify attack types while maintaining machine learning efficiency and applicability across different attack scenarios.

1. Adversarial Input Detection Using Image
Processing Techniques (IPT)
Authors:
• Kishor Datta Gupta
• Dipankar Dasgupta
• Zahid Akhtar
Presented by :
Kishor Datta Gupta

2. Adversarial Attack (AA) on AI/ML
Types:
• Poisoning Attack : Manipulate
training data
• Evasion Attack: Manipulate
input data
• Trojan AI : Manipulate AI
Architecture (example:
Changes weights value)
“Manipulation of training
data, Machine Learning
(ML) model architecture,
or manipulate testing
data in a way that will
result in wrong output
from ML”

3. Different Adversarial Traits
Different Attack method has different types of noise/manipulation style

4. Detection of Adversarial Traits(1)
Clean and adversarial images have quantifiable noise difference

5. Detection of Adversarial Traits(2)

6. Detection of Adversarial Traits(3)

7. Methodology

8. Methodology(2)

9. Comparison

10. Summary
Work against adaptive attack
Don’t reduce ML efficiency
Can identify attack type
Applicable for cross-platform
Work for both blackbox and whitebox attack
Our defense has below properties:

11. Q/A