Image Quality Dependent Degradation for AI Systems

• URL: http://arxiv.org/abs/2607.25736v1
• Date: Tue, 28 Jul 2026 14:00:38 GMT
• Title: Image Quality Dependent Degradation for AI Systems
• Abstract summary: Perception is one of the primary applications where neural networks outperform conventional algorithms.<n>A substantial challenge with AI systems is that their output depends heavily on the quality of the input images.<n>We show that we can still avoid the most critical errors in situations of inferior image quality.
• Score: 0.7816377212023298
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Perception is one of the primary applications where neural networks outperform conventional algorithms. One example is AI systems for automated driving, which can detect pedestrians based on image data and avoid them accordingly. A substantial challenge with these AI systems is that their output depends heavily on the quality of the input images. For example, if an image is of inferior quality due to heavy contamination, such as noise or darkness, accurate predictions are hardly feasible. Additionally, various types of errors can occur, each with varying relevance to the trustworthiness of the underlying AI system. In particular, it may be more critical not to detect an existing person than to detect a person where there is none. Therefore, we want to show that we can still avoid the most critical errors in situations of inferior image quality. To achieve this, we aim to establish a fail-degraded system by lowering the network's confidence threshold based on the estimated image quality, enabling it to detect objects more cautiously in uncertain situations. Additionally, we present a novel method for estimating the quality of incoming images by comparing them to the training data using normalizing flows. We will also conduct experiments applying our method to state-of-the-art object detection. In summary, we will present a design strategy for AI-based systems in automated driving that can deal with poor-quality input data without resorting to fallback solutions. Such measures enhance trust in AI-based systems and lead to an increased provision of the AI component.

Related papers

• Addressing Camera Sensors Faults in Vision-Based Navigation: Simulation and Dataset Development [41.94295877935867]
  This study focuses on an interplanetary exploration mission scenario.<n>A comprehensive analysis of potential fault cases in camera sensors used within the VBN pipeline is presented.<n>A simulation framework is introduced to recreate faulty conditions in synthetically generated images, enabling a systematic and controlled reproduction of faulty data.<n>The resulting dataset of fault-injected images provides a valuable tool for training and testing AI-based fault detection algorithms.
  arXiv  Detail & Related papers  (2025-07-03T13:23:22Z)
• RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors [57.81012948133832]
  We present RAID (Robust evaluation of AI-generated image Detectors), a dataset of 72k diverse and highly transferable adversarial examples.<n>Our methodology generates adversarial images that transfer with a high success rate to unseen detectors.<n>Our findings indicate that current state-of-the-art AI-generated image detectors can be easily deceived by adversarial examples.
  arXiv  Detail & Related papers  (2025-06-04T14:16:00Z)
• Epistemic Uncertainty for Generated Image Detection [107.62647907393377]
  We introduce a novel framework for AI-generated image detection through epistemic uncertainty, aiming to address critical security concerns in the era of generative models.<n>Our key insight stems from the observation that distributional discrepancies between training and testing data manifest distinctively in the epistemic uncertainty space of machine learning models.
  arXiv  Detail & Related papers  (2024-12-08T11:32:25Z)
• Addressing Vulnerabilities in AI-Image Detection: Challenges and Proposed Solutions [0.0]
  This study evaluates the effectiveness of convolutional neural networks (CNNs) and DenseNet architectures for detecting AI-generated images.<n>We analyze the impact of updates and modifications such as Gaussian blurring, prompt text changes, and Low-Rank Adaptation (LoRA) on detection accuracy.<n>The findings highlight vulnerabilities in current detection methods and propose strategies to enhance the robustness and reliability of AI-image detection systems.
  arXiv  Detail & Related papers  (2024-11-26T06:35:26Z)
• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• A Sanity Check for AI-generated Image Detection [49.08585395873425]
  We propose AIDE (AI-generated Image DEtector with Hybrid Features) to detect AI-generated images.<n>AIDE achieves +3.5% and +4.6% improvements to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-06-27T17:59:49Z)
• Improving Interpretability and Robustness for the Detection of AI-Generated Images [6.116075037154215]
  We analyze existing state-of-the-art AIGI detection methods based on frozen CLIP embeddings. We show how to interpret them, shedding light on how images produced by various AI generators differ from real ones.
  arXiv  Detail & Related papers  (2024-06-21T10:33:09Z)
• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
  RIGID is a training-free and model-agnostic method for robust AI-generated image detection. RIGID significantly outperforms existing trainingbased and training-free detectors.
  arXiv  Detail & Related papers  (2024-05-30T14:49:54Z)
• Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection [77.3530907443279]
  We propose a novel self-supervised framework to detect objects in degraded low resolution images. Our methods has achieved superior performance compared with existing methods when facing variant degradation situations.
  arXiv  Detail & Related papers  (2022-08-05T09:36:13Z)
• Image Quality Assessment using Contrastive Learning [50.265638572116984]
  We train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We show through extensive experiments that CONTRIQUE achieves competitive performance when compared to state-of-the-art NR image quality models. Our results suggest that powerful quality representations with perceptual relevance can be obtained without requiring large labeled subjective image quality datasets.
  arXiv  Detail & Related papers  (2021-10-25T21:01:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.