Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns

• URL: http://arxiv.org/abs/2212.07326v1
• Date: Wed, 14 Dec 2022 16:46:54 GMT
• Title: Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns
• Abstract summary: We propose a new mathematical model of printing-imaging channel for the authentication of CDP. The results show that even deep learning created copy fakes unknown at the training stage can be reliably authenticated.
• Score: 10.292065384528799
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection. However, the advent of deep learning as a powerful attacking tool has shown that the general authentication schemes are unable to compete and fail against such attacks. In this paper, we propose a new mathematical model of printing-imaging channel for the authentication of CDP together with a new detection scheme based on it. The results show that even deep learning created copy fakes unknown at the training stage can be reliably authenticated based on the proposed approach and using only digital references of CDP during authentication.

Related papers

• Diffusion-Based Authentication of Copy Detection Patterns: A Multimodal Framework with Printer Signature Conditioning [24.87648457099111]
  Formulating authentication as multi-class printer classification over printer signatures lets our model capture fine-grained, device-specific features.<n>We extend ControlNet by repurposing the denoising process for class-conditioned noise prediction.<n>Results show the framework generalises to counterfeit types unseen during training.
  arXiv  Detail & Related papers  (2026-03-09T22:33:44Z)
• Adapter Shield: A Unified Framework with Built-in Authentication for Preventing Unauthorized Zero-Shot Image-to-Image Generation [74.5813283875938]
  Zero-shot image-to-image generation poses substantial risks related to intellectual property violations.<n>This work presents Adapter Shield, the first universal and authentication-integrated solution aimed at defending personal images from misuse.<n>Our method surpasses existing state-of-the-art defenses in blocking unauthorized zero-shot image synthesis.
  arXiv  Detail & Related papers  (2025-11-25T04:49:16Z)
• A Watermark for Auto-Regressive Image Generation Models [50.599325258178254]
  We propose C-reweight, a distortion-free watermarking method explicitly designed for image generation models.<n>C-reweight mitigates retokenization mismatch while preserving image fidelity.
  arXiv  Detail & Related papers  (2025-06-13T00:15:54Z)
• Sample Correlation for Fingerprinting Deep Face Recognition [83.53005932513156]
  We propose a novel model stealing detection method based on SA Corremplelation (SAC) SAC successfully defends against various model stealing attacks in deep face recognition, encompassing face verification and face emotion recognition, exhibiting the highest performance in terms of AUC, p-value and F1 score. We extend our evaluation of SAC-JC to object recognition including Tiny-ImageNet and CIFAR10, which also demonstrates the superior performance of SAC-JC to previous methods.
  arXiv  Detail & Related papers  (2024-12-30T07:37:06Z)
• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• UniForensics: Face Forgery Detection via General Facial Representation [60.5421627990707]
  High-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video network, with a meta-functional face classification for enriched facial representation.
  arXiv  Detail & Related papers  (2024-07-26T20:51:54Z)
• Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach [0.0]
  We introduce an innovative anti-spoofing approach that combines an unsupervised autoencoder with a convolutional block attention module. The scheme has achieved an average BPCER of 0.96% with an APCER of 1.6% for presentation attacks involving various types of spoofed samples.
  arXiv  Detail & Related papers  (2023-11-07T17:19:59Z)
• Anomaly localization for copy detection patterns through print estimations [10.292065384528799]
  Copy detection patterns (CDP) are recent technologies for protecting products from counterfeiting. Deep learning-based fakes have shown to be hardly distinguishable from originals by traditional authentication systems. In this work, we design a machine learning (ML) based authentication system that only requires digital templates and printed original CDP for training.
  arXiv  Detail & Related papers  (2022-09-29T13:18:57Z)
• Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach [10.292065384528799]
  Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. Previous works have shown that Machine Learning (ML) based attacks can produce high-quality fakes. In this work, we study such a performance assuming a supervised learning (SL) setting.
  arXiv  Detail & Related papers  (2022-06-23T15:58:28Z)
• Mobile authentication of copy detection patterns [11.4928367384196]
  This paper aims at investigating the authentication aspects and the resistances to illegal copying of copy detection patterns (CDP) from machine learning perspectives. A special attention is paid to a reliable authentication under the real life verification conditions when the codes are printed on an industrial printer and enrolled via modern mobile phones. The obtained results show that the modern machine-learning approaches and the technical capacities of modern mobile phones allow to reliably authenticate CDP on end-user mobile phones under the considered classes of fakes.
  arXiv  Detail & Related papers  (2022-03-04T16:07:26Z)
• Machine learning attack on copy detection patterns: are 1x1 patterns cloneable? [14.784426936848604]
  We consider a copy attack against the copy detection patterns (CDP) based on machine learning. The experimental based on samples produced on two industrial printers demonstrate that simple detection metrics used in the CDP authentication cannot reliably distinguish the original CDP from their fakes.
  arXiv  Detail & Related papers  (2021-10-05T17:06:28Z)
• Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication [78.165255859254]
  We propose a reversible watermarking algorithm for integrity authentication. The influence of embedding reversible watermarking on the classification performance is less than 0.5%. At the same time, the integrity of the model can be verified by applying the reversible watermarking.
  arXiv  Detail & Related papers  (2021-04-09T09:32:21Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)
• Scalable Backdoor Detection in Neural Networks [61.39635364047679]
  Deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch. We propose a novel trigger reverse-engineering based approach whose computational complexity does not scale with the number of labels, and is based on a measure that is both interpretable and universal across different network and patch types. In experiments, we observe that our method achieves a perfect score in separating Trojaned models from pure models, which is an improvement over the current state-of-the art method.
  arXiv  Detail & Related papers  (2020-06-10T04:12:53Z)
• Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates [57.52763961195292]
  We present a new attack that exploits not only the labelling function of a classifier, but also the certificate generator. The proposed method applies large perturbations that place images far from a class boundary while maintaining the imperceptibility property of adversarial examples.
  arXiv  Detail & Related papers  (2020-03-19T17:59:44Z)

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.