Backdoor as Probe: Test-Time Adversarial Defense for CLIP

arXiv:2609.34641 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-28

Project page: https://robin-wzq.github.io/Backdoor-as-Probe

Terminology

• Accuracy — 36× in this paper · explained in 1 episode
• Robustness — 28× in this paper · explained in 5 episodes
• Adversarial Robustness — 15× in this paper · explained in 3 episodes
• Adversarial Attacks — 14× in this paper · explained in 1 episode
• Adversarial Defense — 12× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 11× in this paper
• Ablation — 8× in this paper · explained in 1 episode
• Benchmark — 7× in this paper · explained in 2 episodes
• Calibration — 6× in this paper · explained in 7 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Cosine Similarity — 5× in this paper · explained in 3 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Zero-Shot Generalization — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Adversarial Examples — 2× in this paper · explained in 2 episodes
• Chinese Academy of Sciences — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 2× in this paper
• Transferability — 2× in this paper · explained in 4 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• AUC — 1× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision Workshops — 1× in this paper
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Difference of Logits Ratio — 1× in this paper
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• In European Conference on Computer Vision — 1× in this paper
• In The International Conference on Learning Representations — 1× in this paper
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 7 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
• D-TPT: Dimensional Entropy Maximization for Calibrating Test-Time Prompt Tuning in Vision-Language Models
• Visual Instruction Tuning
• Fine-Grained Visual Classification of Aircraft
• Attacking Adversarial Attacks as A Defense

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models