Search Results for author:
Found 13 papers, 3 papers with code
CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning
The detection models exhibited vulnerabilities, with FAR rising to 36. 69%, 31. 23%, and 51. 28% under volume control, fading, and noise injection, respectively.
SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism
Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception.
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity.
Mercury: An Automated Remote Side-channel Attack to Nvidia Deep Learning Accelerator
One big concern about the usage of the accelerators is the confidentiality of the deployed models: model inference execution on the accelerators could leak side-channel information, which enables an adversary to preciously recover the model details.
Alleviating the Effect of Data Imbalance on Adversarial Training
This framework consists of two components: (1) a new training strategy inspired by the effective number to guide the model to generate more balanced and informative AEs; (2) a carefully constructed penalty function to force a satisfactory feature space.
Color Backdoor: A Robust Poisoning Attack in Color Space
To make the trigger more imperceptible and human-unnoticeable, a variety of stealthy backdoor attacks have been proposed, some works employ imperceptible perturbations as the backdoor triggers, which restrict the pixel differences of the triggered image and clean image.
A Benchmark of Long-tailed Instance Segmentation with Noisy Labels
In this paper, we consider the instance segmentation task on a long-tailed dataset, which contains label noise, i. e., some of the annotations are incorrect.
ShiftNAS: Towards Automatic Generation of Advanced Mulitplication-Less Neural Networks
Multiplication-less neural networks significantly reduce the time and energy cost on the hardware platform, as the compute-intensive multiplications are replaced with lightweight bit-shift operations.
Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving
However, DNN models are vulnerable to different types of adversarial attacks, which pose significant risks to the security and safety of the vehicles and passengers.
Towards Robust Point Cloud Models with Context-Consistency Network and Adaptive Augmentation
Extensive evaluations indicate the integration of the two techniques provides much more robustness than existing defense solutions for 3D models.
Practical and Private Heterogeneous Federated Learning
Heterogeneous federated learning (HFL) enables clients with different computation/communication capabilities to collaboratively train their own customized models, in which the knowledge of models is shared via clients' predictions on a public dataset.
Fingerprinting Generative Adversarial Networks
In this paper, we present the first fingerprinting scheme for the Intellectual Property (IP) protection of GANs.
Topology-aware Differential Privacy for Decentralized Image Classification
In this paper, we design Top-DP, a novel solution to optimize the differential privacy protection of decentralized image classification systems.
