## 1 Introduction
To tackle these issues, several researchers have proposed employing deep neural networks for the purpose of image denoising. Firstly, [PERSON] et al. [4] proposed the application of convolutional neural networks (CNNs) for image denoising. [PERSON] et al. [5] employed residual learning and batch normalization techniques in deep convolutional neural networks for image denoising, resulting in an enhanced convergence speed and a superior performance even with deeper network architectures. [PERSON] et al. [6] proposed a noise extraction network based on a multilayer perceptron model. Specifically, the utilization of a multilayer network enables the simulation of complex functions with high recognition accuracy and grouping efficiency; however, it may require a longer training time. [PERSON] et al. [7] proposed a PRNU extraction algorithm based on third-party-weighted sparse coding, which incorporates two weight matrices into the traditional sparse coding model to enhance the accuracy of image noise computation and thereby extract pure PRNU fingerprints. [PERSON] et al. [8] proposed a PRNU extraction algorithm based on a deep iterative scaling convolutional neural network, which optimizes GPU memory utilization and enhances receptive fields by iteratively adjusting the resolution of feature maps. [PERSON] et al. [9] proposed a PRNU extraction algorithm based on a multi-stage progressive neural network, which employs an encoder-decoderarchitecture for learning contextual features and incorporates a high-resolution branch to extract local features. [PERSON] et al. [10] proposed a PRNU fingerprint extraction algorithm based on a densely connected hierarchical network, which introduces dense connections and residual learning, and repeatedly uses the previous feature map as input to effectively solve the gradient vanishing problem during network training. The aforementioned studies comprehensively demonstrate a robust positive correlation between the quality of PRNU fingerprint extraction and the performance of digital image denoising filters. However, these studies are plagued by challenges such as a high number of model parameters. To tackle this issue, this paper proposes an invertible denoising network (InvDN)-based [11] PRNU fingerprint extraction algorithm. The InvDN introduces the application of invertible networks in real-world image denoising, enabling loss-less information preservation and efficient feature extraction. It effectively transforms the input noisy image into a low-resolution clean image while preserving the noisy latent representation. If the first three channels of the transformed image exhibit identical characteristics to the low-resolution image achieved through down-sampling, the high-frequency information is subsequently encoded into the remaining channels. By utilizing the noise present in high-frequency information, the InvDN eliminates all high-frequency representations prior to inversion and reconstructs pristine images based on low-resolution counterparts. This algorithm utilizes a collaborative augmentation process to estimate the PRNU fingerprint by extracting noise residuals from multiple digital images captured by the same imaging device. The paper is structured as follows: In Section 2.1, we present the PRNU extraction algorithm based on the invertible denoising network. Section 2.2 presents an introduction to the fundamental structure and design of the InvDN. Subsequently, in Section 3.1, we introduce the datasets and evaluation metrics employed in this study, while Section 3.2 provides an analysis, comparison, and summary of the experimental results and algorithm performance. Finally, our conclusions are presented in Section 4.