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Found 17 papers, 7 papers with code
Misusing Tools in Large Language Models With Visual Adversarial Examples
Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities.
SkillFence: A Systems Approach to Practically Mitigating Voice-Based Confusion Attacks
Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones.
Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance
Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers.
Exploring Adversarial Robustness of Deep Metric Learning
Deep Metric Learning (DML), a widely-used technique, involves learning a distance metric between pairs of samples.
Adversarial Deep Metric Learning
To the best of our knowledge, we are the first to systematically analyze this dependence effect and propose a principled approach for robust training of deep metric learning networks that accounts for the nuances of metric losses.
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems
In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning.
Data Privacy in Trigger-Action Systems
Trigger-action platforms (TAPs) allow users to connect independent web-based or IoT services to achieve useful automation.
Cryptography and Security
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
By contrast, we contribute a procedure to generate, for the first time, physical adversarial examples that are invisible to human eyes.
GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems
We address three key requirements for practical attacks for the real-world: 1) automatically constraining the size and shape of the attack so it can be applied with stickers, 2) transform-robustness, i. e., robustness of a attack to environmental physical variations such as viewpoint and lighting changes, and 3) supporting attacks in not only white-box, but also black-box hard-label scenarios, so that the adversary can attack proprietary models.
Analyzing the Interpretability Robustness of Self-Explaining Models
Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness.
Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities
Based on a study of five IoT programming platforms, we identify the key insights resulting from works in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues.
Cryptography and Security Programming Languages
Physical Adversarial Examples for Object Detectors
In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene.
Robust Physical-World Attacks on Deep Learning Visual Classification
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input.
Tyche: Risk-Based Permissions for Smart Home Platforms
When using risk-based permissions, device operations are grouped into units of similar risk, and users grant apps access to devices at that risk-based granularity.
Cryptography and Security
Note on Attacking Object Detectors with Adversarial Stickers
Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples.
Robust Physical-World Attacks on Deep Learning Models
We propose a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions.
Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges?
The Internet of Things (IoT) is a new computing paradigm that spans wearable devices, homes, hospitals, cities, transportation, and critical infrastructure.
Cryptography and Security
