Using Anisotropic Diffusion for Efficient Extraction of Sensor Noise in Camera Identification

Abstract

Each digital camera has an intrinsic fingerprint that is unique to each camera. This device fingerprint can be extracted from an image and can be compared with a reference device fingerprint to determine the device origin. The complexity of the filters proposed to accomplish this is increasing. In this note, we use a relatively simple algorithm to extract the sensor noise from images. It has the advantages of being easy to implement and parallelize, and working faster than the wavelet filter that is common for this application. In addition, we compare the performance with a simple median filter and assess whether a previously proposed fingerprint enhancement technique improves results. Experiments are performed on approximately 7500 images originating from 69 cameras, and the results are compared with this often used wavelet filter. Despite the simplicity of the proposed method, the performance exceeds the common wavelet filter and reduces the time needed for the extraction.

On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation

“…The 8-neighbouring pixel approximation of PMD was adopted here. And, we implemented the central difference approximation, as described in the appendix of [26], which is a touch different from the original work [27]. In the TV filters, the forward finite difference approximation of the gradient was used in our implementation.…”

“…In this subsection, we briefly outline these seminal filters: 1) Perona-Malik diffusion (PMD) filter: The filter was adopted in PRNU estimation in [26]. Perona and Malik presented this filter in [27] based on the anisotropic diffusion equation:…”

“…In their discrete form of (15) (|∇ ( , , )|) is the diffusion coefficient that is chosen to preserve edges and textures. One of the two Perona and Malik's diffusion coefficients was chosen in [26] owing to its better performance in PRNU extraction. That is,…”

“…The PeronaMalik's discrete form of the right hand side of (15) where ℳ = {( , + 1), ( , − 1), ( + 1, ), ( , )}, |ℳ| = 4 (an 8-neighbouring pixel approximation can also be adopted) and ∈ (0,1] determines the rate of diffusion. In [26], was fixed to 1/7, and the number of scales was set to three, i.e. ̂( , ) = ( , , 3) − ( , , 0).…”

Enhancing Sensor Pattern Noise for Source Camera Identification

“…Abundant studies are devoted to improving the performance of SPN based source identification. Some of them focus on finding the optimal denoising filter for SPN extraction [1,4,15,5], while some others focus on increasing the reliability of the extracted SPN (SPN enhancing).…”