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Found 9 papers, 2 papers with code
BadEdit: Backdooring large language models by model editing
It boasts superiority over existing backdoor injection techniques in several areas: (1) Practicality: BadEdit necessitates only a minimal dataset for injection (15 samples).
MIP: CLIP-based Image Reconstruction from PEFT Gradients
Contrastive Language-Image Pre-training (CLIP) model, as an effective pre-trained multimodal neural network, has been widely used in distributed machine learning tasks, especially Federated Learning (FL).
Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models
Existing black-box attacks have demonstrated promising potential in creating adversarial examples (AE) to deceive deep learning models.
Omnipotent Adversarial Training in the Wild
We first introduce an oracle into the adversarial training process to help the model learn a correct data-label conditional distribution.
Multi-target Backdoor Attacks for Code Pre-trained Models
We evaluate our approach on two code understanding tasks and three code generation tasks over seven datasets.
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.
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models
The key feature of our attack is that the adversary does not need prior information about the downstream tasks when implanting the backdoor to the pre-trained model.
Stealing Deep Reinforcement Learning Models for Fun and Profit
This paper presents the first model extraction attack against Deep Reinforcement Learning (DRL), which enables an external adversary to precisely recover a black-box DRL model only from its interaction with the environment.
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed.
