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Found 9 papers, 5 papers with code
Black-box Adversarial Attacks Against Image Quality Assessment Models
Specifically, we first formulate the attack problem as maximizing the deviation between the estimated quality scores of original and perturbed images, while restricting the perturbed image distortions for visual quality preservation.
Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks
In this paper, we make the first attempt to evaluate the robustness of NR-VQA models against adversarial attacks, and propose a patch-based random search method for black-box attack.
Payload-Independent Direct Cost Learning for Image Steganography
This framework directly learns universal costs that can be applied to any payload.
StarVQA+: Co-training Space-Time Attention for Video Quality Assessment
Self-attention based Transformer has achieved great success in many computer vision tasks.
HVS Revisited: A Comprehensive Video Quality Assessment Framework
Based on the revisited HVS, a no-reference VQA framework called HVS-5M (NRVQA framework with five modules simulating HVS with five characteristics) is proposed.
Ranked #6 on
Video Quality Assessment
on LIVE-FB LSVQ
Multi-instance Point Cloud Registration by Efficient Correspondence Clustering
We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud.
Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge
To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure.
Image Steganography based on Iteratively Adversarial Samples of A Synchronized-directions Sub-image
In this paper, we present a novel steganography scheme denoted as ITE-SYN (based on ITEratively adversarial perturbations onto a SYNchronized-directions sub-image), by which security data is embedded with synchronizing modification directions to enhance security and then iteratively increased perturbations are added onto a sub-image to reduce loss with cover class label of the target CNN classifier.
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment.
