arXiv AI
Anchoring Bias: A Persistent Fairness Backdoor Attack against MLLMs under Continual Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
3d ago
Time to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake Detection
The paper examines how continual learning (CL) techniques used to update deepfake detectors over time affect fairness across different demographic groups. By training detectors on a chronological series of 11 real-world deepfake generation methods, the authors track four fairness dimensions and find that accuracy loss often worsens unfairness, while fairness gains are rare. The study reveals that inequitable forgetting is linked to representation learning rather than mere underrepresentation, urging CL methods to consider social outcomes when balancing stability and plasticity.
arXiv AI
Aug 26
Not All Tokens Are Equal: Region-Aware Consistency Repair of Backdoors in MLLMs
arXiv:2608.24354v1 Announce Type: cross Abstract: MLLMs are increasingly deployed in user-facing applications, yet they inherit backdoor risks from the pipelines used to construct them: triggers may...
arXiv AI
Jul 24
Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning
arXiv:2607. 21300v1 Announce Type: cross Abstract: Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations.
arXiv Computation and Language
Aug 25
Forgetting to Forget: Attention Sink as A Gateway for Backdooring LLM Unlearning
arXiv:2510.17021v2 Announce Type: replace-cross Abstract: Large language model (LLM) unlearning is a key approach for removing undesired data, knowledge, or behaviors from pretrained models while ret...
arXiv AI
Jun 2
Perturbation Effects on Accuracy and Fairness among Similar Individuals
arXiv:2404. 01356v3 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.
arXiv AI
Jul 1
Perturbation Effects on Robustness and Individual Fairness
arXiv:2404. 01356v4 Announce Type: replace-cross Abstract: Deep neural networks are vulnerable to adversarial perturbations that can simultaneously degrade prediction robustness and individual fairness across diverse application settings.