Mobile authentication of copy detection patterns: how critical is to know fakes?

Taran, Olga; Tutt, Joakim; Holotyak, Taras; Chaban, Roman; Bonev, Slavi; Voloshynovskiy, Slava

doi:10.1109/wifs53200.2021.9648398

Cited by 9 publications

(7 citation statements)

References 10 publications

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“…Similarly, [15] employed a one class support vector machine (OC-SVM) for the authentication method. The research aimed to examine the feasibility of CDP authentication under real-world conditions, by using codes that were printed on an industrial printer and captured by a modern mobile phone.…”

Section: Cdp Methodsmentioning

confidence: 99%

CNN-Based Model for Copy Detection Pattern Estimation and Authentication

Alfarozi

Pratama²

2023

JNTETI

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Counterfeiting has been one of the crimes of the 21st century. One of the methods to overcome product counterfeiting is a copy detection pattern (CDP) stamped on the product. CDP is a copy-sensitive pattern that leads to quality degradation of the pattern after the print and scan process. The amount of information loss is used to distinguish between original and fake CDPs. This paper proposed a CDP estimation model based on the convolutional neural network (CNN), namely, CDP-CNN. The CDP-CNN addresses the spatial dependency of the image patch. Thus, it should be better than the state-of-the-art model that uses a multi-layer perceptron (MLP) architecture. The proposed model had an estimation bit error rate (BER) of 9.91% on the batch estimation method. The error rate was 9% lower than the previous method that used an autoencoder MLP model. The proposed model also had a lower number of parameters compared to the previous method. The effect of preprocessing, namely the use of an unsharp mask, was tested using a statistical testing method. The effect of preprocessing had no significant difference except in the batch estimation scheme where the unsharp mask filter reduced the error rate by at least 0.5%. In addition, the proposed model was also used for the authentication method. The authentication using the estimation model had a good separation distribution to distinguish the fake and original CDPs. Thus, the CDP can still be used as the authentication method with reliable performance. It helps anti-counterfeiting on product distribution and reduces negative impacts on various sectors of the economy.

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Section: Cdp Methodsmentioning

confidence: 99%

CNN-Based Model for Copy Detection Pattern Estimation and Authentication

Alfarozi

Pratama²

2023

JNTETI

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“…Recent research works showed the vulnerability of CS-GCs based approaches to deep-learning based estimation attacks [43], [1]. Nevertheless, the countermeasures based on pre-processing technique [16] and the use of similarity metrics combinations [35] showed the usability of such solution in real-world applications for packaging protection. 3) Forensics approach.…”

Section: Overview Of Existing Printing Solutionsmentioning

confidence: 99%

“…This solution is interesting as it gives good results in open and closed set conditions. All the mentioned solutions, especially the recent forensics systems [27], [6] and CSGC based authentication systems [16], [35], achieve good identification/authentication results. Nevertheless, almost all cited solutions were tested on coated or uncoated paper using laser printers.…”

Section: Overview Of Existing Printing Solutionsmentioning

confidence: 99%

Authentication of rotogravure print-outs using a regular test pattern

Tkachenko

Trémeau

Fournel

2022

Journal of Information Security and Applications

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“…An attacker accesses the original printed CDP in the public domain and estimates, for an observed original x d i , the digital template t i as td i through a ML-based estimation model [4,5,6,7]. Having the estimation td i , the attacker is then able to produce a fake code f a/d i ∈ R w×h using one of the attacker's printing processes a ∈ A, |A| = A.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The defender has to design an authentication system that, given original or fake code y i , predicts its authenticity with respect to t i . cloned under certain conditions [3,4,5]. More recent works, which used industrial printers such as HP Indigo capable of producing symbols of size 1x1 at the resolution of 812.8 dpi, have shown that attacks based on ML-methods make the authentication based on hand-crafted features and SVM-based classifiers harder for the defender [6,7].…”

Section: Introductionmentioning

confidence: 99%

Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach

Pulfer¹,

Chaban²,

Belousov³

et al. 2022

Preprint

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Copy detection patterns (CDP) are an attractive technology that allows manufacturers to defend their products against counterfeiting. The main assumption behind the protection mechanism of CDP is that these codes printed with the smallest symbol size (1x1) on an industrial printer cannot be copied or cloned with sufficient accuracy due to data processing inequality. However, previous works have shown that Machine Learning (ML) based attacks can produce high-quality fakes, resulting in decreased accuracy of authentication based on traditional feature-based authentication systems. While Deep Learning (DL) can be used as a part of the authentication system, to the best of our knowledge, none of the previous works has studied the performance of a DL-based authentication system against ML-based attacks on CDP with 1x1 symbol size. In this work, we study such a performance assuming a supervised learning (SL) setting.

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Mobile authentication of copy detection patterns: how critical is to know fakes?

Cited by 9 publications

References 10 publications

CNN-Based Model for Copy Detection Pattern Estimation and Authentication

CNN-Based Model for Copy Detection Pattern Estimation and Authentication

Authentication of rotogravure print-outs using a regular test pattern

Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach

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