Search Results for author:
Found 7 papers, 3 papers with code
Ensemble-based Blackbox Attacks on Dense Prediction
We performed a number of experiments for object detectors and segmentation to highlight the significance of the our proposed methods.
Disguise without Disruption: Utility-Preserving Face De-Identification
We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.
Blackbox Attacks via Surrogate Ensemble Search
We also show that the perturbations generated by our proposed method are highly transferable and can be adopted for hard-label blackbox attacks.
Zero-Query Transfer Attacks on Context-Aware Object Detectors
We compare our zero-query attack against a few-query scheme that repeatedly checks if the victim system is fooled.
Context-Aware Transfer Attacks for Object Detection
In this paper, we present a new approach to generate context-aware attacks for object detectors.
Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes
Vision systems that deploy Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples.
Solving Phase Retrieval with a Learned Reference
We performed a number of simulations on a variety of datasets under different conditions and found that our proposed method for phase retrieval via unrolled network and learned reference provides near-perfect recovery at fixed (small) computational cost.
