ComplicitSplat: Downstream Models are Vulnerable to Blackbox Attacks by 3D Gaussian Splat Camouflages
As 3D Gaussian Splatting (3DGS) gains rapid adoption in safety-critical tasks for efficient novel-view synthesis from static images, how might an adversary tamper images to cause harm? We introduce ComplicitSplat, the first attack that exploits standard 3DGS shading methods to create viewpoint-specific camouflage - colors and textures that change with viewing angle - to embed adversarial content in scene objects that are visible only from specific viewpoints and without requiring access to model architecture or weights. Our extensive experiments show that ComplicitSplat generalizes to successfully attack a variety of popular detector - both single-stage, multi-stage, and transformer-based models on both real-world capture of physical objects and synthetic scenes. To our knowledge, this is the first black-box attack on downstream object detectors using 3DGS, exposing a novel safety risk for applications like autonomous navigation and other mission-critical robotic systems.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Label-Only Model Inversion Attacks via Boundary Repulsion
Recent studies show that the state-of-the-art deep neural networks are vulnerable to model inversion attacks, in which access to a model is abused to reconstruct private training data of any given target class. Existing …
Face RecognitionmodelQuantifying (Hyper) Parameter Leakage in Machine Learning
Machine Learning models, extensively used for various multimedia applications, are offered to users as a blackbox service on the Cloud on a pay-per-query basis. Such blackbox models are commercially valuable to adversari…
BIG-bench Machine LearningInference AttackModel extractionInvestigating Adversarial Robustness against Preprocessing used in Blackbox Face Recognition
Face Recognition (FR) models have been shown to be vulnerable to adversarial examples that subtly alter benign facial images, exposing blind spots in these systems, as well as protecting user privacy. End-to-end FR syste…
Adversarial RobustnessFace RecognitionFace DetectionBlackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
Recent research on reinforcement learning (RL) has suggested that trained agents are vulnerable to maliciously crafted adversarial samples. In this work, we show how such samples can be generalised from White-box and Gre…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Time Series AnalysisAn Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is also increasingly adopted in pervasive comp…
Autonomous Driving