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Found 21 papers, 13 papers with code
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis
Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others.
Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbations
What if robustness in perceptual models is not something to learn but something to design?
Cross-Modal Transferable Image-to-Video Attack on Video Quality Metrics
Recent studies have revealed that modern image and video quality assessment (IQA/VQA) metrics are vulnerable to adversarial attacks.
Accelerated zero-order SGD under high-order smoothness and overparameterized regime
Thus we suppose that only a black-box access to the function values of the objective is available, possibly corrupted by adversarial noise: deterministic or stochastic.
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment
Our method is based on Median Smoothing (MS) combined with an additional convolution denoiser with ranking loss to improve the SROCC and PLCC scores of the defended IQA metric.
Exploring adversarial robustness of JPEG AI: methodology, comparison and new methods
Adversarial robustness of neural networks is an increasingly important area of research, combining studies on computer vision models, large language models (LLMs), and others.
AIM 2024 Challenge on Compressed Video Quality Assessment: Methods and Results
The challenge aimed to evaluate the performance of VQA methods on a diverse dataset of 459 videos, encoded with 14 codecs of various compression standards (AVC/H. 264, HEVC/H. 265, AV1, and VVC/H. 266) and containing a comprehensive collection of compression artifacts.
Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics
In the field of Image Quality Assessment (IQA), the adversarial robustness of the metrics poses a critical concern.
Ti-Patch: Tiled Physical Adversarial Patch for no-reference video quality metrics
This paper proposes a new method for testing quality metrics vulnerability in the physical space.
Adversarial purification for no-reference image-quality metrics: applicability study and new methods
In this study, we aim to cover that case and check the transferability of adversarial purification defences from image classifiers to IQA methods.
IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics
No-reference image- and video-quality metrics are widely used in video processing benchmarks.
Towards adversarial robustness verification of no-reference image-and video-quality metrics
In this paper, we propose a new method of analysing the stability of modern deep image- and video-quality metrics to different adversarial attacks.
Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks
Nowadays, neural-network-based image- and video-quality metrics perform better than traditional methods.
Fast Adversarial CNN-based Perturbation Attack on No-Reference Image- and Video-Quality Metrics
The proposed attack (FACPA) can be exploited as a preprocessing step in real-time video processing and compression algorithms.
Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics
Modern neural-network-based no-reference image- and video-quality metrics exhibit performance as high as full-reference metrics.
Video compression dataset and benchmark of learning-based video-quality metrics
Video-quality measurement is a critical task in video processing.
Universal Perturbation Attack on Differentiable No-Reference Image- and Video-Quality Metrics
Indeed, if an attack can confuse the metric, an attacker can easily increase quality scores.
ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
The ERQA metric, which we propose in this paper, aims to estimate a model's ability to restore real details using VSR.
Ranked #22 on
Video Quality Assessment
on MSU SR-QA Dataset
Shot boundary detection method based on a new extensive dataset and mixed features
Shot boundary detection in video is one of the key stages of video data processing.
Objective video quality metrics application to video codecs comparisons: choosing the best for subjective quality estimation
Quality assessment plays a key role in creating and comparing video compression algorithms.
Hacking VMAF and VMAF NEG: vulnerability to different preprocessing methods
We also show that some preprocessing methods can increase VMAF NEG scores by up to 23. 6%.
