Search Results for author:
Found 11 papers, 7 papers with code
Mitigating Unauthorized Speech Synthesis for Voice Protection
With just a few speech samples, it is possible to perfectly replicate a speaker's voice in recent years, while malicious voice exploitation (e. g., telecom fraud for illegal financial gain) has brought huge hazards in our daily lives.
Query-Efficient Video Adversarial Attack with Stylized Logo
Therefore, to generate adversarial examples with a low budget and to provide them with a higher verisimilitude, we propose a novel black-box video attack framework, called Stylized Logo Attack (SLA).
GRFormer: Grouped Residual Self-Attention for Lightweight Single Image Super-Resolution
The code is available at \url{https://github. com/sisrformer/GRFormer}.
Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
In contrast to widely studied sophisticated attacks in the field, we propose an effective yet easy-to-launch physical adversarial attack, named AdvColor, against black-box face recognition pipelines in the physical world.
Effects of Exponential Gaussian Distribution on (Double Sampling) Randomized Smoothing
Randomized Smoothing (RS) is currently a scalable certified defense method providing robustness certification against adversarial examples.
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer
In this paper, we propose an attack on ASR systems based on user-customized style transfer.
Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security
Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities that increasingly influence various aspects of our daily lives, constantly defining the new boundary of Artificial General Intelligence (AGI).
LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model
Benefiting from the popularity and scalably usability of Segment Anything Model (SAM), we first extract different regions according to semantic information and then track them through the video stream to maintain the temporal consistency.
3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
To overcome this limitation and enhance the reconstruction of 3D structural features, we propose an innovative approach that integrates existing 2D features with 3D features to guide the model learning process.
LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer
We separate the attack into three stages: style reference selection, reinforcement-learning-based logo style transfer, and perturbation optimization.
StyleFool: Fooling Video Classification Systems via Style Transfer
In this paper, we focus on unrestricted perturbations and propose StyleFool, a black-box video adversarial attack via style transfer to fool the video classification system.
