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cs.CV2026

Fast Test-Time Refinement for Robust Learned Image Compression

Jiaming Liang, Chi-Man Pun, Weisi Lin

Learned image compression (LIC) has demonstrated remarkable rate-distortion (RD) performance in benign settings. However, the high representational capacity endowed by deep neural…

cs.CV2026

Control Your View: High-Resolution Global Semantic Manipulation in Learned Image Compression

Jiaming Liang, Chi-Man Pun, Weisi Lin +1

Learned image compression (LIC) integrates deep neural networks (DNNs) to map high-dimensional images into compact latent representations, reducing redundancy and achieving superio…

cs.CV2026

SDDF: Specificity-Driven Dynamic Focusing for Open-Vocabulary Camouflaged Object Detection

Jiaming Liang, Yifeng Zhan, Chunlin Liu +6

Open-vocabulary object detection (OVOD) aims to detect known and unknown objects in the open world by leveraging text prompts. Benefiting from the emergence of large-scale vision--…

cs.CV2026

Fast Preemptive Robustification: High-Frequency Response Anti-Aligns Shared Vulnerability

Jiaming Liang, Chi-Man Pun

Adversarial attacks can readily compromise deep neural networks (DNNs). In particular, transferable attacks (TAs) exploit the shared vulnerabilities among DNNs, enabling perturbati…

cs.CV2026

A Unified Spatial Alignment Framework for Highly Transferable Transformation-Based Attacks on Spatially Structured Tasks

Jiaming Liang, Chi-Man Pun

Transformation-based adversarial attacks (TAAs) demonstrate strong transferability when deceiving classification models. However, existing TAAs often perform unsatisfactorily or ev…

cs.CV2026

OTI: A Model-free and Visually Interpretable Measure of Image Attackability

Jiaming Liang, Haowei Liu, Chi-Man Pun

Despite the tremendous success of neural networks, benign images can be corrupted by adversarial perturbations to deceive these models. Intriguingly, images differ in their attacka…

cs.CV2025

Dynamic Parameter Optimization for Highly Transferable Transformation-Based Attacks

Jiaming Liang, Chi-Man Pun

Despite their wide application, the vulnerabilities of deep neural networks raise societal concerns. Among them, transformation-based attacks have demonstrated notable success in t…