PRNU Anonymous Algorithm Used for Privacy Protection in Biometric Authentication Systems

PRNU Anonymous Algorithm Used for Privacy Protection in Biometric Authentication Systems

Jian Li, Xiaobo Zhang, Bin Ma, Meihong Yang, Chunpeng Wang, Yang Liu, Xinan Cui, Xiaotong Yang
Copyright: © 2023 |Pages: 19
DOI: 10.4018/IJSWIS.317928
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The photo response non-uniformity (PRNU) is used to connect an image to its source sensor. In this paper, researchers propose a PRNU anonymity method based on image segmentation to cut the relationship between the image and its source camera. According to the distribution rule of PRNU in the high and low frequency band of the image, the high and low frequency information of the part is also processed differently, which ensures the quality of the output image to a large extent. Experiments on the datasets show that the proposed method can preserve the biometric characteristics of the device while maintaining the anonymity of the device. Comparing with prior art, peak signal to noise ratio (PSNR) and cosine similarity are improved by 1.9 dB and 0.02 points, respectively.
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Privacy might leak when people hand their personal images or videos to an authentication system for entrance permission (Narang et al., 2020). Traditional solutions usually focus on the protection of multimedia contents—for instance, faces. However, the device that is used to capture an image can expose one’s privacy, too. Sensor pattern noise (SPN)-based algorithms are able to distinguish a camera and then find the owner, raising a host of new questions about personal privacy and anonymity. Therefore, it is particularly important to anonymize the source attributes of an image while preserving the biometric utility in some specific biometric scenarios.

Limitations of Prior Art

Device anonymization needs us to remove the SPN from image. The main component of SPN is photo response non-uniformity (PRNU) noise. PRNU is a signal with a rather wide frequency spectrum. In this light, an appropriate frequency domain analysis method is important to achieve the balance between the PRNU anonymization and high image quality.

The traditional PRNU masking method has the following disadvantages:

  • In the classical Fourier Transform domain, the signal expression is not intuitive, and the suppression effect of PRNU is weak. The compression based on discrete cosine transform (DCT) generally appears as a block effect, which will affect the image quality.

  • For biometric images, the existing anonymous algorithms are more complex and require a large number of expensive training images. Therefore, anonymous methods need to be balanced and efficient in practical applications.

Proposed Work

Generally, the area around the edge is misunderstood when only weaker noise filters are used, such as Wiener filters or median filters. The residual noise obtained by the wavelet transform filter also contains the fewest scene features. Therefore, an edge filter based on wavelet and Wiener filters is selected.

In this paper, we propose a simple method to anonymize the device while preserving biometric utility. The approach we propose has two dimensions. First, the original image is separated from the eyes or face. In view of the different levels of importance of the human eye (or face) and other regions in a biometric image for identity recognition, we conduct eye separation to strengthen the identity pass rate. Second, the image anonymity is carried out.

Advantages Over the Prior Art

The object of our work is to develop an algorithm to cover the sensor fingerprint (PRNU) while preserving the visual quality and biometric utility. The advantages of the proposed method are as follows:

  • 1.

    An algorithm based on discrete wavelet transform (DWT) is proposed, which can perform different processing for high- and low-frequency bands of PRNU hierarchically. This algorithm ensures that anonymity has a higher success rate even in light conditions.

  • 2.

    Image segmentation is applied to ensure that the processed test image preserves more original details. In particular, its biometric content achieves only slight degradation, preserving more biometric utility of the image.

  • 3.

    This method not only achieves the anonymity of PRNU but also overcomes some defects of existing algorithms. The PSNR and cosine similarity between the output image and the original image are improved by 2.4 dB and 0.1 percentage points on average, respectively.

The rest of this paper is organized as follows. We introduce some methods about the traditional PRNU anonymity and PRNU extraction. We then describe the proposed method for PRNU anonymization. Next, we describe the datasets, experimental results, and comparisons. We then show the future development and application of this work and conclude the paper.

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