Papers Real-World Adversarial Attack
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Large Language Models (LLMs) are increasingly deployed as computer-use agents, autonomously performing tasks within real desktop or web environments. While this evolution greatly expands practical use cases for humans, i…
Adversarial AttackAI and SafetyReal-World Adversarial AttackRed Teaming+1To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now
The recent advances in diffusion models (DMs) have revolutionized the generation of realistic and complex images. However, these models also introduce potential safety hazards, such as producing harmful content and infri…
Adversarial RobustnessBenchmarkingReal-World Adversarial AttackPatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification
Backdoor attack is a major threat to deep learning systems in safety-critical scenarios, which aims to trigger misbehavior of neural network models under attacker-controlled conditions. However, most backdoor attacks hav…
Adversarial AttackBackdoor AttackReal-World Adversarial AttackKidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches
Autonomous flying robots, such as multirotors, often rely on deep learning models that make predictions based on a camera image, e.g. for pose estimation. These models can predict surprising results if applied to input i…
Deep LearningPose EstimationReal-World Adversarial AttackRobot NavigationSimultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
Adversarial patch is an important form of real-world adversarial attack that brings serious risks to the robustness of deep neural networks. Previous methods generate adversarial patches by either optimizing their pertur…
Adversarial AttackFace RecognitionPositionReal-World Adversarial Attack+1Ignore Previous Prompt: Attack Techniques For Language Models
Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications. However, studies that explore their vulnerabilities emerging from malic…
Adversarial AttackAdversarial TextLanguage ModelingLanguage Modelling+2DTA: Physical Camouflage Attacks using Differentiable Transformation Network
To perform adversarial attacks in the physical world, many studies have proposed adversarial camouflage, a method to hide a target object by applying camouflage patterns on 3D object surfaces. For obtaining optimal physi…
Adversarial AttackObjectobject-detectionObject Detection+1Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
Object detection plays a key role in many security-critical systems. Adversarial patch attacks, which are easy to implement in the physical world, pose a serious threat to state-of-the-art object detectors. Developing re…
Adversarial Attack DetectionAdversarial DefenseAdversarial RobustnessObject+3Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
Deep learning-based facial recognition (FR) models have demonstrated state-of-the-art performance in the past few years, even when wearing protective medical face masks became commonplace during the COVID-19 pandemic. Gi…
Adversarial AttackFace RecognitionReal-World Adversarial AttackAttack on practical speaker verification system using universal adversarial perturbations
In authentication scenarios, applications of practical speaker verification systems usually require a person to read a dynamic authentication text. Previous studies played an audio adversarial example as a digital signal…
Real-World Adversarial AttackRoom Impulse Response (RIR)Speaker Verificationspeech-recognition+1Enhancing Real-World Adversarial Patches through 3D Modeling of Complex Target Scenes
Adversarial examples have proven to be a concerning threat to deep learning models, particularly in the image domain. However, while many studies have examined adversarial examples in the real world, most of them relied …
Adversarial AttackInference AttackObject ReconstructionReal-World Adversarial AttackTaking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders
In recent years, machine learning has become prevalent in numerous tasks, including algorithmic trading. Stock market traders utilize machine learning models to predict the market's behavior and execute an investment str…
Adversarial AttackAlgorithmic TradingBIG-bench Machine LearningInference Attack+1Adversarial Music: Real World Audio Adversary Against Wake-word Detection System
Voice Assistants (VAs) such as Amazon Alexa or Google Assistant rely on wake-word detection to respond to people's commands, which could potentially be vulnerable to audio adversarial examples. In this work, we target ou…
Adversarial AttackReal-World Adversarial AttackReal-world adversarial attack on MTCNN face detection system
Recent studies proved that deep learning approaches achieve remarkable results on face detection task. On the other hand, the advances gave rise to a new problem associated with the security of the deep convolutional neu…
Adversarial AttackFace DetectionReal-World Adversarial AttackAdvHat: Real-world adversarial attack on ArcFace Face ID system
In this paper we propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the rectangular paper sticker on a common co…
Adversarial AttackReal-World Adversarial Attack