Copertina

Transparency Attacks: How Imperceptible Image Layers Can Fool AI Perception

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	McKee, Forrest, Noever, David
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Computer Vision and Pattern Recognition Cryptography and Security Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Exploiting Alpha Transparency In Language And Vision-Based AI Systems
di: Noever, David, et al.
Pubblicazione: (2024)

Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
di: Qraitem, Maan, et al.
Pubblicazione: (2024)

Can a Teenager Fool an AI? Evaluating Low-Cost Cosmetic Attacks on Age Estimation Systems
di: Shen, Xingyu, et al.
Pubblicazione: (2026)

IU: Imperceptible Universal Backdoor Attack
di: Lin, Hsin, et al.
Pubblicazione: (2026)

Hallucinating AI Hijacking Attack: Large Language Models and Malicious Code Recommenders
di: Noever, David, et al.
Pubblicazione: (2024)

Infecting Generative AI With Viruses
di: Noever, David, et al.
Pubblicazione: (2025)

Novel AI Camera Camouflage: Face Cloaking Without Full Disguise
di: Noever, David, et al.
Pubblicazione: (2024)

Safeguarding Voice Privacy: Harnessing Near-Ultrasonic Interference To Protect Against Unauthorized Audio Recording
di: McKee, Forrest, et al.
Pubblicazione: (2024)

Favicon Trojans: Executable Steganography Via Ico Alpha Channel Exploitation
di: Noever, David, et al.
Pubblicazione: (2025)

Certified but Fooled! Breaking Certified Defences with Ghost Certificates
di: Vo, Quoc Viet, et al.
Pubblicazione: (2025)

Perturbing Attention Gives You More Bang for the Buck: Subtle Imaging Perturbations That Efficiently Fool Customized Diffusion Models
di: Xu, Jingyao, et al.
Pubblicazione: (2024)

Beauty and the Beast: Imperceptible Perturbations Against Diffusion-Based Face Swapping via Directional Attribute Editing
di: Huang, Yilong, et al.
Pubblicazione: (2026)

Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems
di: Zhou, Xugui, et al.
Pubblicazione: (2023)

Image Can Bring Your Memory Back: A Novel Multi-Modal Guided Attack against Image Generation Model Unlearning
di: Liu, Renyang, et al.
Pubblicazione: (2025)

DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers
di: Martínez-Mejía, C. A., et al.
Pubblicazione: (2024)

On the Detection of Image-Scaling Attacks in Machine Learning
di: Quiring, Erwin, et al.
Pubblicazione: (2023)

IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learning
di: Wang, Jiayao, et al.
Pubblicazione: (2025)

AICAttack: Adversarial Image Captioning Attack with Attention-Based Optimization
di: Li, Jiyao, et al.
Pubblicazione: (2024)

UnMarker: A Universal Attack on Defensive Image Watermarking
di: Kassis, Andre, et al.
Pubblicazione: (2024)

One-Index Vector Quantization Based Adversarial Attack on Image Classification
di: Fan, Haiju, et al.
Pubblicazione: (2024)

FameBias: Embedding Manipulation Bias Attack in Text-to-Image Models
di: Roh, Jaechul, et al.
Pubblicazione: (2024)

Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks
di: Struppek, Lukas, et al.
Pubblicazione: (2023)

SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images
di: Fan, Mingyuan, et al.
Pubblicazione: (2024)

LISArD: Learning Image Similarity to Defend Against Gray-box Adversarial Attacks
di: Costa, Joana C., et al.
Pubblicazione: (2025)

StyleFool: Fooling Video Classification Systems via Style Transfer
di: Cao, Yuxin, et al.
Pubblicazione: (2022)

Transferability of Adversarial Attacks in Video-based MLLMs: A Cross-modal Image-to-Video Approach
di: Huang, Linhao, et al.
Pubblicazione: (2025)

AI-generated Image Detection: Passive or Watermark?
di: Guo, Moyang, et al.
Pubblicazione: (2024)

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2024)

QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformers
di: Baras, Amit, et al.
Pubblicazione: (2023)

Attack Anything: Blind DNNs via Universal Background Adversarial Attack
di: Lian, Jiawei, et al.
Pubblicazione: (2024)

Technical Report for ICML 2024 TiFA Workshop MLLM Attack Challenge: Suffix Injection and Projected Gradient Descent Can Easily Fool An MLLM
di: Guo, Yangyang, et al.
Pubblicazione: (2024)

Universal Backdoor Attacks
di: Schneider, Benjamin, et al.
Pubblicazione: (2023)

Explainable Differential Privacy-Hyperdimensional Computing for Balancing Privacy and Transparency in Additive Manufacturing Monitoring
di: Piran, Fardin Jalil, et al.
Pubblicazione: (2024)

Provenance of AI-Generated Images: A Vector Similarity and Blockchain-based Approach
di: Sharma, Jitendra, et al.
Pubblicazione: (2025)

Memory Backdoor Attacks on Neural Networks
di: Luzon, Eden, et al.
Pubblicazione: (2024)

Invisible Backdoor Attacks on Diffusion Models
di: Li, Sen, et al.
Pubblicazione: (2024)

Backdoor Attack with Sparse and Invisible Trigger
di: Gao, Yinghua, et al.
Pubblicazione: (2023)

Can AI Freelancers Compete? Benchmarking Earnings, Reliability, and Task Success at Scale
di: Noever, David, et al.
Pubblicazione: (2025)

Sparse and Transferable Universal Singular Vectors Attack
di: Kuvshinova, Kseniia, et al.
Pubblicazione: (2024)

Energy-Latency Attacks via Sponge Poisoning
di: Cinà, Antonio Emanuele, et al.
Pubblicazione: (2022)