Some Remarks about Tracing Digital Cameras – Faster Method and Usable Countermeasure

2020

In this paper we consider the problem of a privacy threat enabling tracing digital cameras by the analysis of pictures they produced. As thousands of images are processed at a mass scale, the threat may apply to most users of digital cameras. We consider a state-of-theart algorithm for digital camera identification proposed in Lucas et al. (IEEE Trans Inf Forensics Secur 1(2):205-214, 2006) and discuss strategies that can be used to bypass it, in order to make information about the camera unavailable. It turns out that many natural strategies like Gaussian blur, adding artificial noise or removing pixels' least significant bit from the image does not prevent the identification of a camera unless a huge loss of image details is suffered. On the other hand, we show a method to bypass the camera identification with a just marginally more complex, yet not intuitive, method namely cropping the image on the edges and resizing to the original size using Lanczos resampling.

Section: Introductionmentioning

confidence: 63%

Section: Previous and Related Workmentioning

confidence: 99%

On robustness of camera identification algorithms

2020

“…This paper is a continuation of research presented in [3] and in [2]. In [3] there have been proposed two algorithms called PSNR-CT and DEPECHE. The PSNR-CT algorithm is used for an ultra-fast camera identification (compared to Lukás et al [27]).…”

Section: Introductionmentioning

confidence: 86%

Robustness of digital camera identification with convolutional neural networks

2021

This paper considers the area of digital forensics (DF). One of the problem in DF is the issue of identification of digital cameras based on images. This aspect has been attractive in recent years due to popularity of social media platforms like Facebook, Twitter etc., where lots of photographs are shared. Although many algorithms and methods for digital camera identification have been proposed, there is lack of research about their robustness. Therefore, in this paper the robustness of digital camera identification with the use of convolutional neural network is discussed. It is assumed that images may be of poor quality, for example, degraded by Poisson noise, Gaussian blur, random noise or removing pixels’ least significant bit. Experimental evaluation conducted on two large image datasets (including Dresden Image Database) confirms usefulness of proposed method, where noised images are recognized with almost the same high accuracy as normal images.

“…In [2] we have proposed a fast method for camera tracing based on Peak signal-to-noise ratio. Method is very fast in comparison with [31], however classification efficiency is lower.…”

Section: Related and Previous Workmentioning

confidence: 99%

“…It is worth saying that Lukás et al's algorithm achieves better performance in case of older devices with CCD sensors. In [2] it was shown that performance of this algorithm is lower for newer cameras with CMOS sensors. Nowadays, recent cameras have CMOS sensors instead of CCDs.…”

Section: Experiments I -Brand Identification By Analyzing Vignettingmentioning

confidence: 99%

Digital camera identification based on analysis of optical defects

2019

Self Cite

In this paper we deal with the problem of digital camera identification by photographs. Identifying camera is possible by analyzing camera's sensor artifacts that occur during the process of photo processing. The problem of digital camera identification has been popular for a long time. Recently many effective and robust algorithms for solving this problem have been proposed. However, almost all solutions are based on state-of-the-art algorithm, proposed by Lukás et al. in 2006. Core of this algorithm is to calculate the so-called sensor pattern noise based on denoising images with wavelet-based denoising filter. Such technique is very efficient, but very time consuming. In this paper we consider tracing cameras by analyzing defects of their optical systems, like vignetting and lens distortion. We show that analysis of vignetting defect allows for recognizing brand of the camera. Lens distortion can be used to distinguish images from different cameras. Experimental evaluation was carried out on 60 devices (compact cameras and smartphones) for a total number of 12 051 images, with support of the Dresden Image Database. Proposed methods do not require denoising images with wavelet-based denoising filter what has a significant influence for speed of image processing, compared with state-of-the-art algorithm.