Preprint
Rearchitecting Classification Frameworks For Increased Robustness
ArXiv Preprint
Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu
A case study and evaluation on how deep neural networks (DNNs) are highly effective but vulnerable to adversarial inputs -- subtle manipulations designed to deceive them. Existing defenses often sacrifice accuracy and require extensive training. Collaborating with the lead author, I implemented a design of a hierarchical classification approach that leverages invariant features to enhance adversarial robustness without compromising accuracy.
Rearchitecting Classification Frameworks For Increased Robustness
ArXiv Preprint
Varun Chandrasekaran, Brian Tang, Nicolas Papernot, Kassem Fawaz, Somesh Jha, Xi Wu
A case study and evaluation on how deep neural networks (DNNs) are highly effective but vulnerable to adversarial inputs -- subtle manipulations designed to deceive them. Existing defenses often sacrifice accuracy and require extensive training. Collaborating with the lead author, I implemented a design of a hierarchical classification approach that leverages invariant features to enhance adversarial robustness without compromising accuracy.
