This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.

A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

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.

/pdf/determining-image-origin-and-integrity-using-sensor-noise-375kg5mshh.pdf

Determining Image Origin and Integrity Using Sensor Noise

We are undoubtedly living in an age where we are exposed to a remarkable array of visual imagery. While we may have historically had confidence in the integrity of this imagery, today's digital technology has begun to erode this trust. From the tabloid magazines to the fashion industry and in mainstream media outlets, scientific journals, political campaigns, courtrooms, and the photo hoaxes that land in our e-mail in-boxes, doctored photographs are appearing with a growing frequency and sophistication. Over the past five years, the field of digital forensics has emerged to help restore some trust to digital images. The author reviews the state of the art in this new and exciting field.

Image forgery detection

Steganography, the art of hiding of information in apparently innocuous objects or images, is a field with a rich heritage, and an area of rapid current development. This clear, self-contained guide shows you how to understand the building blocks of covert communication in digital media files and how to apply the techniques in practice, including those of steganalysis, the detection of steganography. Assuming only a basic knowledge in calculus and statistics, the book blends the various strands of steganography, including information theory, coding, signal estimation and detection, and statistical signal processing. Experiments on real media files demonstrate the performance of the techniques in real life, and most techniques are supplied with pseudo-code, making it easy to implement the algorithms. The book is ideal for students taking courses on steganography and information hiding, and is also a useful reference for engineers and practitioners working in media security and information assurance.

http://www.gbv.de/dms/ilmenau/toc/608077828.PDF

Steganography in Digital Media: Principles, Algorithms, and Applications

Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm – classifiers based on rich media models. Existing network architectures, however, still contain elements designed by hand, such as fixed or constrained convolutional kernels, heuristic initialization of kernels, the thresholded linear unit that mimics truncation in rich models, quantization of feature maps, and awareness of JPEG phase. In this work, we describe a deep residual architecture designed to minimize the use of heuristics and externally enforced elements that is universal in the sense that it provides state-of-the-art detection accuracy for both spatial-domain and JPEG steganography. The key part of the proposed architecture is a significantly expanded front part of the detector that “computes noise residuals” in which pooling has been disabled to prevent suppression of the stego signal. Extensive experiments show the superior performance of this network with a significant improvement, especially in the JPEG domain. Further performance boost is observed by supplying the selection channel as a second channel.

Deep Residual Network for Steganalysis of Digital Images

Photo-response non-uniformity (PRNU) of digital sensors was recently proposed [1] as a unique identification fingerprint
for digital cameras. The PRNU extracted from a specific image can be used to link it to the digital camera that took the
image. Because digital camcorders use the same imaging sensors, in this paper, we extend this technique for
identification of digital camcorders from video clips. We also investigate the problem of determining whether two video
clips came from the same camcorder and the problem of whether two differently transcoded versions of one movie came
from the same camcorder. The identification technique is a joint estimation and detection procedure consisting of two
steps: (1) estimation of PRNUs from video clips using the Maximum Likelihood Estimator and (2) detecting the presence
of PRNU using normalized cross-correlation. We anticipate this technology to be an essential tool for fighting piracy of
motion pictures. Experimental results demonstrate the reliability and generality of our approach.

/pdf/source-digital-camcorder-identification-using-sensor-photo-49x5u1l8po.pdf

Source digital camcorder identification using sensor photo response non-uniformity

Detection of modern JPEG steganographic algorithms has traditionally relied on features aware of the JPEG phase. In this paper, we port JPEG-phase awareness into the architecture of a convolutional neural network to boost the detection accuracy of such detectors. Another innovative concept introduced into the detector is the "catalyst kernel" that, together with traditional high-pass filters used to pre-process images allows the network to learn kernels more relevant for detection of stego signal introduced by JPEG steganography. Experiments with J-UNIWARD and UED-JC embedding algorithms are used to demonstrate the merit of the proposed design.

https://dl.acm.org/doi/pdf/10.1145/3082031.3083248

JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images

In this paper, we revisit the problem of digital camera sensor identification using photo-response non-uniformity noise
(PRNU). Considering the identification task as a joint estimation and detection problem, we use a simplified model for
the sensor output and then derive a Maximum Likelihood estimator of the PRNU. The model is also used to design
optimal test statistics for detection of PRNU in a specific image. To estimate unknown shaping factors and determine
the distribution of the test statistics for the image-camera match, we construct a predictor of the test statistics on small
image blocks. This enables us to obtain conservative estimates of false rejection rates for each image under Neyman-
Pearson testing. We also point out a few pitfalls in camera identification using PRNU and ways to overcome them by
preprocessing the estimated PRNU before identification.

/pdf/digital-imaging-sensor-identification-further-study-1c792b209m.pdf

Mo Chen

Papers

Determining Image Origin and Integrity Using Sensor Noise

Deep Residual Network for Steganalysis of Digital Images

Source digital camcorder identification using sensor photo response non-uniformity

JPEG-Phase-Aware Convolutional Neural Network for Steganalysis of JPEG Images

Digital imaging sensor identification (further study)