A lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance and reduces sensitive-attribute attacker accuracy, improves fairness metrics such as demographic parity difference and equalized odds, and maintains competitive task performance.
Abstract
This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness while preserving task performance. FairNVT is motivated by the intuition that reducing sensitive-attribute information in the representation used by the downstream classifier can facilitate fairer predictions. Our approach learns task-relevant and sensitive embeddings via lightweight adapters, applies calibrated Gaussian noise to the sensitive embedding, and fuses it with the task representation. Together with orthogonality constraints and fairness regularization, these components jointly reduce sensitive-attribute leakage in the learned embeddings and encourage fairer downstream predictions. Across three datasets spanning vision and language, FairNVT reduces sensitive-attribute attacker accuracy, improves fairness metrics such as demographic parity difference and equalized odds, and maintains competitive task performance.
A novel demographically balanced FDD benchmark is constructed to train and evaluate the fairness of detectors under both balanced and imbalanced population scenarios, and a novel expression and demographic perceptual vision-language model is introduced, termed FairForensics, for generalizable fair deepfake detection.
Yaning Zhang, Jiao Wu, Zan Gao et al.· 0 citations
Orthogonal Semantic Decoupling is proposed, a framework that decouples demographic semantics from forgery cues and out-performs state-of-the-art methods in both generalization and fairness, breaking the trade-off.
Zhongyu Shi, Siyu Peng, Yimin Kang et al.· 0 citations
This work establishes a high-probability generalization bound for ViTs in classification tasks under adversarial settings, and elucidates the roles of several factors in mitigating perturbation effects, norm regularization of weight matrices and depth-wise propagation constraints on layer-wise norms.
Ziwen Jiang, Chang Cao, Han Li et al.· 0 citations
This paper proposes AutoNorm-S (Stabilized), a training strategy that mitigates optimization instability through a gate-freezing schedule, and suggests that decoupling normalization selection from optimization noise provides a practical and principled approach for adaptive normalization in Transformer architectures.
While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks, posing significant security risks. Existing defense methods predominantly target single-task scenarios (e.g., zero-shot classification) and consequently lack generalizability across various multimodal tasks. To address this limitation, we propose a dual adversarial fine-tuning framework that jointly optimizes visual and semantic supervision signals from two modalities, enhancing model robustness while generalizing across multiple downstream tasks. The proposed framework comprises two core components, i.e., $\textbf{Visual}$ supervision branch and $\textbf{Semantic}$ supervision branch. The former branch leverages features from clean images, extracted via a frozen original vision encoder, to guide adversarial robustness while the latter incorporates caption-image alignment as a contextual signal to preserve semantic coherence under attack. Moreover, our method achieves cross-task robustness by simply replacing the CLIP vision encoder in the original model, with no need of separate task-specific retraining or architecture modifications.Extensive experiments demonstrate that our approach outperforms the state-of-the-art method in adversarial robustness evaluation across zero-shot classification, image captioning, and visual question answering (VQA) tasks.
Sibo Wang, Jie Zhang, Shiguang Shan et al.· 0 citations
Deploying deep learning models on resource-constrained edge devices necessitates a trade-off between computational efficiency and robustness. While Knowledge Distillation (KD) is widely used to compress large models into lightweight students, the impact of the Teacher’s architectural inductive bias on the Student’s safety profile remains underexplored. This paper investigates Cross-Architecture Distillation, specifically examining whether the robustness properties of a Vision Transformer (ViT) Teacher can be transferred to a Convolutional Neural Network (CNN) Student (MobileNetV2). Using Centered Kernel Alignment (CKA) and Fourier Spectral Analysis, we demonstrate that robustness transfer is driven by the learning of global shape biases— preserving the teacher's functional geometry—rather than strict feature mimicry. To isolate architectural bias from model capacity confounds and validate scalability to high-resolution data, our experiments are conducted on ImageNet-1k. Results reveal that a MobileNetV2 distilled from a ViT-B/16 Teacher achieves a significantly lower mean corruption error (mCE) compared to one distilled from a massive, capacity-matched ResNet-152 (p < 0.0001, Cohen's d = 21.50), despite comparable clean accuracy. Furthermore, we show that the ViT-distilled student effectively suppresses high-frequency noise, validating its resilience against adversarial perturbations. These findings establish a pareto-optimal strategy for deploying robust, lightweight models, proving that the choice of Teacher architecture is a critical hyperparameter for safety-critical edge applications.
Tamuno Opubo Dappa, Somtochukwu Anunobi· International Conference on...· 0 citations
Known for his clear and elegant writing style, Bertsekas shaped fields from control and optimization to large-scale computation and artificial intelligence.
