Accurate lora-finetuning quantization of llms via information retention
The LoRA-finetuning quantization of LLMs has been extensively studied to obtain accurate
yet compact LLMs for deployment on resource-constrained hardware. However, existing …
yet compact LLMs for deployment on resource-constrained hardware. However, existing …
Physical Adversarial Patch Attack for Optical Fine-Grained Aircraft Recognition
Deep neural networks (DNNs) have been widely used in remote sensing but demonstrated
to be sensitive with adversarial examples. By introducing carefully designed perturbations to …
to be sensitive with adversarial examples. By introducing carefully designed perturbations to …
Behavior Backdoor for Deep Learning Models
The various post-processing methods for deep-learning-based models, such as
quantification, pruning, and fine-tuning, play an increasingly important role in artificial …
quantification, pruning, and fine-tuning, play an increasingly important role in artificial …
DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time
Physical adversarial examples (PAEs) are regarded as" whistle-blowers" of real-world risks
in deep-learning applications. However, current PAE generation studies show limited …
in deep-learning applications. However, current PAE generation studies show limited …
Image Forensics Strikes Back: Defense Against Adversarial Patch
Traffic sign recognition plays a crucial role in self-driving cars, but unfortunately, it is
vulnerable to adversarial patches (AP). Although AP can efficiently fool DNN-based models …
vulnerable to adversarial patches (AP). Although AP can efficiently fool DNN-based models …