ECCV2026 AI Safety AI paper notes paper summaries Adversarial Robustness Video Generation Few-/Zero-Shot Learning Object Detection Segmentation Personalized Generation

🛡️ AI Safety¶

🎞️ ECCV2026 · 9 paper notes

📌 Same area in other venues: 📷 CVPR2026 (145) · 🔬 ICLR2026 (141) · 💬 ACL2026 (5) · 🧪 ICML2026 (114) · 🤖 AAAI2026 (45) · 🧠 NeurIPS2025 (73)

🔥 Top topics: Adversarial Robustness ×3 · Video Generation ×2

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP: Proposes A4D, a completely black-box, zero-shot adversarial attack detection framework. By exploiting the sensitivity of CLIP to micro-perturbations, the framework compares the cosine similarity of image embeddings with a set of carefully crafted text prompts, and then aggregates them into a single detection score via PCA. This detects adversarial examples without needing to know the attack type or classifier architecture, achieving SOTA performance across multiple attacks, datasets, and classifiers.
Adversarial Attack and Disturbance Detection by Hadamard-Coded Output Representations for Object Detection and Semantic Segmentation: HadamardNet replaces conventional class outputs with redundant codewords and uses probability-simplex projection to produce both class probabilities and an inconsistency residual, enabling single-pass perturbation detection without guaranteeing that predictions on disturbed inputs are correct.
Anti-Prompt: Image Protection against Text-Guided Image-to-Video Generation: Anti-Prompt protects an owner's images with small perturbations that weaken text conditioning, increase the relative dominance of visual pathways, and disrupt image encoding; on white-box CogVideoX evaluation, its video assessment average is 3.25 versus 3.92 for reproduced I2VGuard, where lower scores indicate stronger protection.
AracNet: Revealing Debiasing Signals across Layers with Shallow Monitors: AracNet trains shallow monitors on a frozen biased classifier, selects the layer whose ground-truth confidence distribution best exposes bias, and reweights training of a reinitialized classifier, reaching \(91.27\pm0.56\%\) worst-group accuracy on Waterbirds without relying on a narrowly timed early-stopping point for bias mining.
Exploiting Local Flatness for Efficient Out-of-Distribution Detection: This paper provides the first systematic analysis of the difference in loss landscape curvature between OOD and ID samples, discovering that OOD inputs exhibit larger Hessian curvature which increases as the distribution shift intensifies. Based on this, a lightweight OOD detector, Fold, is proposed, which replaces expensive parameter-space curvature approximations with a feature-space Hessian. This is paired with partial feature normalization to enhance ID-OOD separability, and a self-supervised logit masking scheme (AutoFold) is introduced to automatically calibrate the normalization parameter. Fold achieves an average AUROC improvement of 1.63% and a 2.30% reduction in FPR95 across multiple benchmarks, with a computational overhead comparable to a single forward pass.
Improving Adversarial Robustness via Activation Amplification and Attenuation: This paper proposes A3 (Activation Amplification and Attenuation), a lightweight learnable activation scaling module that implements two modes—activation amplification and attenuation—using the same set of parameters. During training, the degraded predictions from the amplification mode are utilized as negative references to construct contrastive and ranking losses. During inference, only the attenuation mode is employed to enhance adversarial robustness without introducing significant computational overhead.
IREU: Identity-Related Encoder-Only Unlearning for Customized Portrait Generation: IREU introduces the identity unlearning problem for Customized Portrait Generation (CPG) for the first time. By leveraging Face-Swap to localize identity-related dimensions in the embedding space and executing feature perturbations strictly along these dimensions, it establishes an unlearning pipeline that only updates the image encoder to erase target identity generation capabilities while preserving the fidelity of other identities. Furthermore, the unlearned encoder can be transferred to different CPG generators with zero fine-tuning.
Moiré Video Authentication: A Physical Signature Against AI Video Generation: This paper proposes using moiré interference patterns as a physical signature to authenticate video authenticity: a compact double-layer grating structure is placed in the shooting scene, and the Pearson correlation coefficient between the fringe phase change and the translational displacement of the grating image is calculated to distinguish real videos from AI-generated ones. In real videos, these two are strictly coupled by the laws of optical geometry (mean correlation coefficient of 0.87), whereas even the strongest current video generation models (Veo 3.1, Grok Imagine, LTX-2) fail to precisely reproduce this coupling even under optimal configurations (mean correlation coefficient of 0.57, Cohen's d of 1.71, indicating highly significant differences).
ProtoFair: Fair Self-Supervised Contrastive Learning via Pseudo-Counterfactual Pairs: ProtoFair proposes a plug-and-play fairness regularization term. Without modifying existing self-supervised contrastive learning objectives, it identifies "same semantic content but different sensitive groups" pseudo-counterfactual pairs through momentum-updated unsupervised clustering prototypes, pulling these cross-group samples closer in the embedding space to force the encoder to learn representations invariant to sensitive attributes. Combined with SimCLR / SupCon / BarlowTwins / BYOL on CelebA, UTKFace, and NIH Chest X-rays, it significantly reduces Equalized Odds while maintaining competitive accuracy.