It is well known that rotation introduces periodic artifacts into the resulting image. By measuring such periodicities, the rotation angle θ can be estimated from the rotated image without the availability of the original unrotated image. However, existing methods suffer from harmonics, especially when θ is small. This paper revisits the harmonics caused by rotation in the cyclic spectrum and points out that such harmonics can help our rotation angle estimation if used effectively. Based on this observation, we propose aggregating the magnitudes of the candidate peaks and their harmonics in detecting the rotation-specific peak. Both theoretical analyses and experimental results demonstrate the advantage of the proposed scheme over previous methods.

Performance of camera identification methods based on PRNU is very sensitive to geometric operations applied to images during acquisition and processing. Handling images that have been geometrically transformed, such as rotated, downsampled, and/or cropped requires overcoming pixel desynchronization problem. This work expands applicability of camera identification methods based on PRNU to the class of HDR images. Geometric transformations in HDR images revealed in this work are reversed in a series of steps involving block-wise PRNU matching. Efficiency of this method is then tested on HDR images from publicly available UNIFI dataset spanning 26 cameras of mobile devices.

Sensor photoresponse nonuniformity has been proposed as a unique identifier (fingerprint) for various forensic tasks, including digital-camera ballistics in which an image is matched to the specific camera that took it. The problem investigated here concerns the situation when an adversary estimates the sensor fingerprint from a set of images and superimposes it onto an image from a different camera to frame an innocent victim. This paper proposes a reliable method for detecting such fake fingerprints under rather mild and general assumptions about the adversary's activity and the means available to the victim. The proposed method is subjected to experiments to evaluate its reliability as well as its limitations. The conclusion that can be made from this study is that planting a sensor fingerprint in an image without leaving a trace is significantly more difficult than previously thought.

Due to a production error, the above titled paper (ibid., vol. 6, no. 1, pp. 227-236, Mar. 11), was published as a correspondence in the March 2011 issue of IEEE Transactions on Information Forensics and Security. This paper was actually accepted as a Regular Paper and should have been published as such.

Sensor photo-response non-uniformity (PRNU) was introduced by Lukáš et al. [1] to solve the problem of digital camera sensor identification. The PRNU is the main component of a camera fingerprint that can reliably identify a specific camera. This fingerprint can be estimated from multiple images taken by the camera. In this paper, we demonstrate that the same fingerprint can be used for identification of camera brand and model. This is possible due to the fact that fingerprints estimated from images in the TIFF/JPEG format contain local structure due to various in-camera processing that can be detected by extracting a set of numerical features from the fingerprints and classifying them using pattern classification methods. We estimate and classify fingerprints for more than 4500 digital cameras spanning 8 different brands and 17 models. The average probability of correctly classified camera brand was 90.8%.

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

In this paper, we propose a method for verifying whether two digital images were obtained using the same digital camera. The method uses test statistics derived from a two-channel detector taking as input the noise residuals from both images. It is not assumed that the camera that took the images is available.

Wet paper codes were previously proposed as a tool for construction of steganographic schemes with arbitrary (nonshared) selection channels. In this paper, we propose a new approach to wet paper codes using random linear codes of small codimension that at the same time improves the embedding efficiency (number of random message bits embedded per embedding change). Practical algorithms are given and their performance is evaluated experimentally and compared to theoretically achievable bounds. An approximate formula for the embedding efficiency of the proposed scheme is derived. The proposed coding method can be modularly combined with most steganographic schemes to improve their security.

In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using a denoising filter. To identify the camera from a given image, we consider the reference pattern noise as a spread-spectrum watermark, whose presence in the image is established by using a correlation detector. Experiments on approximately 320 images taken with nine consumer digital cameras are used to estimate false alarm rates and false rejection rates. Additionally, we study how the error rates change with common image processing, such as JPEG compression or gamma correction.

The problem investigated in this paper is identification of sensor that was used to obtain a given digital image. We show that the high-medium frequency component of the sensor pattern noise is an equivalent of "bullet scratches" for digital images and can be used for reliable forensic identification. For each sensor, we first calculate its reference pattern (an estimate of the sensor pattern noise) by averaging the noise component from multiple images. This pattern serves as a unique identification fingerprint whose presence in a given image is established using a correlation detector. The proposed identification technique was tested on several thousand images obtained by nine digital cameras. In all cases, we were able to correctly identify the camera that took the image. We also show that it is possible to identify the camera from images subjected to combined processing, including lossy JPEG compression, gamma correction, recoloring, and resizing.

We propose a new fragile watermark for image authentication. Based on the Yeung-Mintzer (see Proc. ICIP'97, Santa Barbara, California, 1997) scheme, the new watermark does not have certain security gaps common to many previously proposed fragile watermarks. A block cipher is used instead of binary look-up tables. Pixel values are perturbed by small quantities so that the cipher maps small pixel neighborhoods to a fixed binary logo. This process is further modified in order to embed image indices (time stamps) into disjoint blocks of every image. This is necessary for detection of collages from multiple authenticated images. We also formulate basic security requirements and investigate the security of the new scheme.

In this paper, we introduce two techniques for self-embedding an image in itself as a means for protecting the image content. After self-embedding, it is possible to recover portions of the image that have been cropped out, replaced, damaged, or otherwise tampered without accessing the original image. The first method is based on transforming small 8/spl times/8 blocks using a DCT, quantizing the coefficients, and carefully encoding them in the least significant bits of other, distant squares. This method provides very high quality of reconstruction but it is very fragile. The quality of the reconstructed image areas is roughly equivalent to a 50% quality JPEG compressed original. The second method uses a principle similar to differential encoding to embed a circular shift of the original image with decreased color depth into the original image. The quality of the reconstructed image gradually degrades with increasing amount of noise in the tampered image. The first technique can also be used as a fragile watermark for image authentication, while the second technique can be classified as a semi-robust watermark.