Neural polarizer: A lightweight and effective backdoor defense via purifying poisoned features
Recent studies have demonstrated the susceptibility of deep neural networks to backdoor
attacks. Given a backdoored model, its prediction of a poisoned sample with trigger will be …
attacks. Given a backdoored model, its prediction of a poisoned sample with trigger will be …
Shared adversarial unlearning: Backdoor mitigation by unlearning shared adversarial examples
Backdoor attacks are serious security threats to machine learning models where an
adversary can inject poisoned samples into the training set, causing a backdoored model …
adversary can inject poisoned samples into the training set, causing a backdoored model …
Backdoor Attacks and Defenses Targeting Multi-Domain AI Models: A Comprehensive Review
Since the emergence of security concerns in artificial intelligence (AI), there has been
significant attention devoted to the examination of backdoor attacks. Attackers can utilize …
significant attention devoted to the examination of backdoor attacks. Attackers can utilize …
Enhancing fine-tuning based backdoor defense with sharpness-aware minimization
Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced
by attackers, is becoming increasingly critical for machine learning security and integrity …
by attackers, is becoming increasingly critical for machine learning security and integrity …
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Abstract Contrastive Vision-Language Pre-training known as CLIP has shown promising
effectiveness in addressing downstream image recognition tasks. However recent works …
effectiveness in addressing downstream image recognition tasks. However recent works …
Not all prompts are secure: A switchable backdoor attack against pre-trained vision transfomers
Given the power of vision transformers a new learning paradigm pre-training and then
prompting makes it more efficient and effective to address downstream visual recognition …
prompting makes it more efficient and effective to address downstream visual recognition …
Tat: Targeted backdoor attacks against visual object tracking
Visual object tracking (VOT) is a fundamental computer vision task that aims to track a target
in a sequence of video frames. It has been broadly adopted in safety-and security-critical …
in a sequence of video frames. It has been broadly adopted in safety-and security-critical …
Pointcrt: Detecting backdoor in 3d point cloud via corruption robustness
Backdoor attacks for point clouds have elicited mounting interest with the proliferation of
deep learning. The point cloud classifiers can be vulnerable to malicious actors who seek to …
deep learning. The point cloud classifiers can be vulnerable to malicious actors who seek to …
Follow-your-click: Open-domain regional image animation via short prompts
Despite recent advances in image-to-video generation, better controllability and local
animation are less explored. Most existing image-to-video methods are not locally aware …
animation are less explored. Most existing image-to-video methods are not locally aware …
Backdoor Attacks to Deep Neural Networks: A Survey of the Literature, Challenges, and Future Research Directions
O Mengara, A Avila, TH Falk - IEEE Access, 2024 - ieeexplore.ieee.org
Deep neural network (DNN) classifiers are potent instruments that can be used in various
security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that …
security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that …