In this paper, we address the issue of digital camera identification, a key area in digital forensics. This problem is well-known in the literature, with many algorithms proposed for identifying digital cameras based on their fingerprints. However, there is a notable lack of methods that provide fast and accurate digital camera identification, especially given the large image sizes produced by modern digital cameras. Additionally, fingerprints are typically represented as matrices of a size corresponding to the camera’s input images, which can pose storage challenges for forensic centers. Therefore, we propose a method that utilizes a fully convolutional network (FCN) for digital camera identification which is faster than traditional convolutional neural networks (CNN), equipped with fully connected layers. Moreover, we also show that the proposed network can detect lens aberrations, including lens vignetting and distortion. Extensive experimental evaluation conducted on many cameras and images demonstrates the reliability of the proposed method.

Abstract 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-the-art 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.

Abstract 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.

Abstract 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.

Abstract In this paper we deal with the problem of calculating Automatic Exposure (AE) in digital cameras. The main problem that often occurs when taking pictures is correct exposure setting. Typically, smartphones with built-in cameras, as well as “cheap” compact digital cameras do not offer possibility of manual exposure setting. The reason is that users do not have knowledge how to set the optimal exposure, or just simply do not want to do this. Therefore, it forces that user has to rely on automatic exposure algorithms implemented in the camera. Unfortunately, these algorithms often do not perform well what causes improperly exposed images. In this paper, new algorithms for automatic exposure are proposed with the special focus on minimizing overexposed areas in the images. We have implemented proposed algorithms and conducted experiments for their efficiency, comparing with some modern cameras or smartphones. Experimental verification (enhanced by statistical analysis) shows that proposed algorithms give statistically less overexposed areas than comparative AEs.