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Introduction

3D adversarial objects

Physical attacks on traffic signs

Adversarial Patches to Attack Person Detection

Semantic Segmentation and Object Detection

LIDAR attack

Definitions

Problem Formulation

Fast Gradient Sign Method • Goodfellow et al. proposed the Fast Gradient Sign Method FGSM

Basic Iterative Method

Carlini-Wagner Attack

Problems and Challenges

Other Problems

Contributions

Trust Region Optimization

Updating the Trust Region

Trust region example - Initial Start

Iteration 1

Final Trajectory after 20 iterations

Proposed Method

Metrics

Types of attacks used

Summary of setup

Time Performance on ImageNet

Qualitative Results

ImageNet Results

Second order attack results

Conclusion

Description:

Explore a comprehensive lecture on trust region-based adversarial attacks on neural networks, presented by the University of Central Florida. Delve into various aspects of adversarial attacks, including 3D objects, physical attacks on traffic signs, and adversarial patches for person detection. Learn about semantic segmentation, object detection, and LIDAR attacks. Understand key concepts such as Fast Gradient Sign Method, Basic Iterative Method, and Carlini-Wagner Attack. Discover the challenges in the field and the proposed solutions using trust region optimization. Examine the metrics, types of attacks, and experimental setup used in the study. Analyze time performance on ImageNet, qualitative results, and second-order attack outcomes. Gain valuable insights into this critical area of machine learning security in this 32-minute presentation.

Trust Region Based Adversarial Attack on Neural Networks

University of Central Florida

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#Computer Science #Machine Learning #Machine Learning Security #Adversarial Attacks #Artificial Intelligence #Computer Vision #Neural Networks #Object Detection #Engineering #GIS #Remote Sensing #LIDAR #Semantic Segmentation

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