Assessing the Viability of Synthetic Physical Copy Detection Patterns on Different Imaging Systems

• URL: http://arxiv.org/abs/2410.02575v1
• Date: Thu, 3 Oct 2024 15:21:35 GMT
• Title: Assessing the Viability of Synthetic Physical Copy Detection Patterns on Different Imaging Systems
• Abstract summary: This paper explores the potential of synthetic physical Copy Detection Patterns (CDP) to improve the robustness of anti-counterfeiting systems. By leveraging synthetic physical CDP, we aim at enhancing security and cost-effectiveness across various real-world applications.
• Score: 4.156535226615695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper explores the potential of synthetic physical Copy Detection Patterns (CDP) to improve the robustness of anti-counterfeiting systems. By leveraging synthetic physical CDP, we aim at enhancing security and cost-effectiveness across various real-world applications. Our research demonstrates that synthetic CDP offer substantial improvements in authentication accuracy compared to one based on traditional digital templates. We conducted extensive tests using both a scanner and a diverse range of mobile phones, validating our approach through ROC analysis. The results indicate that synthetic CDP can reliably differentiate between original and fake samples, making this approach a viable solution for real-world applications, though requires an additional research to make this technology scalable across a variety of imaging devices.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• SynthID-Image: Image watermarking at internet scale [55.5714762895087]
  We introduce SynthID-Image, a deep learning-based system for invisibly watermarking AI-generated imagery.<n>This paper documents the technical desiderata, threat models, and practical challenges of deploying such a system at internet scale.
  arXiv  Detail & Related papers  (2025-10-10T11:03:31Z)
• Deep Learning Models for Robust Facial Liveness Detection [56.08694048252482]
  This study introduces a robust solution through novel deep learning models addressing the deficiencies in contemporary anti-spoofing techniques.<n>By innovatively integrating texture analysis and reflective properties associated with genuine human traits, our models distinguish authentic presence from replicas with remarkable precision.
  arXiv  Detail & Related papers  (2025-08-12T17:19:20Z)
• CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI [58.35348718345307]
  Current efforts to distinguish between real and AI-generated images may lack generalization. We propose a novel framework, Co-Spy, that first enhances existing semantic features. We also create Co-Spy-Bench, a comprehensive dataset comprising 5 real image datasets and 22 state-of-the-art generative models.
  arXiv  Detail & Related papers  (2025-03-24T01:59:29Z)
• Is JPEG AI going to change image forensics? [50.92778618091496]
  We investigate the counter-forensic effects of the new JPEG AI standard based on neural image compression. Our results demonstrate a reduction in the performance of leading forensic detectors when analyzing content processed through JPEG AI.
  arXiv  Detail & Related papers  (2024-12-04T12:07:20Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  Synthetic Data Generation based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered. This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images. The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• SONAR: A Synthetic AI-Audio Detection Framework and Benchmark [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark. It aims to provide a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content. It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based deepfake detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• Provable Performance Guarantees of Copy Detection Patterns [4.671529048076975]
  Copy Detection Patterns (CDPs) are crucial elements in modern security applications. This paper aims to establish a theoretical framework to derive optimal criteria for the analysis, optimization, and future development of CDP authentication technologies.
  arXiv  Detail & Related papers  (2024-09-26T08:55:44Z)
• Fully Automated OCT-based Tissue Screening System [5.646346784449182]
  The system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery.
  arXiv  Detail & Related papers  (2024-05-15T14:56:17Z)
• Paired Diffusion: Generation of related, synthetic PET-CT-Segmentation scans using Linked Denoising Diffusion Probabilistic Models [0.0]
  This research introduces a novel architecture that is able to generate multiple, related PET-CT-tumour mask pairs using paired networks and conditional encoders. Our approach includes innovative, time step-controlled mechanisms and a noise-seeding' strategy to improve DDPM sampling consistency.
  arXiv  Detail & Related papers  (2024-03-26T14:21:49Z)
• DiffFinger: Advancing Synthetic Fingerprint Generation through Denoising Diffusion Probabilistic Models [0.0]
  This study explores the generation of synthesized fingerprint images using Denoising Diffusion Probabilistic Models (DDPMs) Our results reveal that DiffFinger not only competes with authentic training set data in quality but also provides a richer set of biometric data, reflecting true-to-life variability.
  arXiv  Detail & Related papers  (2024-03-15T14:34:29Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• A Discrepancy Aware Framework for Robust Anomaly Detection [51.710249807397695]
  We present a Discrepancy Aware Framework (DAF), which demonstrates robust performance consistently with simple and cheap strategies. Our method leverages an appearance-agnostic cue to guide the decoder in identifying defects, thereby alleviating its reliance on synthetic appearance. Under the simple synthesis strategies, it outperforms existing methods by a large margin. Furthermore, it also achieves the state-of-the-art localization performance.
  arXiv  Detail & Related papers  (2023-10-11T15:21:40Z)
• Stochastic Digital Twin for Copy Detection Patterns [6.5247605335587915]
  Copy detection patterns (CDP) present an efficient technique for product protection against counterfeiting. Recent advancements in computer modelling, notably the concept of a "digital twin" for printing-imaging channels, allow for enhanced scalability. This paper extends previous research which modelled a printing-imaging channel using a machine learning-based digital twin for CDP.
  arXiv  Detail & Related papers  (2023-09-28T21:38:21Z)
• SIAN: Style-Guided Instance-Adaptive Normalization for Multi-Organ Histopathology Image Synthesis [63.845552349914186]
  We propose a style-guided instance-adaptive normalization (SIAN) to synthesize realistic color distributions and textures for different organs. The four phases work together and are integrated into a generative network to embed image semantics, style, and instance-level boundaries.
  arXiv  Detail & Related papers  (2022-09-02T16:45:46Z)
• Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition [61.87842307164351]
  We first propose an Identity-Aware CycleGAN (IACycleGAN) model that applies a new perceptual loss to supervise the image generation network. It improves CycleGAN on photo-sketch synthesis by paying more attention to the synthesis of key facial regions, such as eyes and nose. We develop a mutual optimization procedure between the synthesis model and the recognition model, which iteratively synthesizes better images by IACycleGAN.
  arXiv  Detail & Related papers  (2021-03-30T01:30:08Z)
• Cognitive Visual Inspection Service for LCD Manufacturing Industry [80.63336968475889]
  This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry. System is based on two cornerstones: robust/high-performance defect recognition model and cognitive visual inspection service architecture.
  arXiv  Detail & Related papers  (2021-01-11T08:14:35Z)

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.