Digital watermarking is a key ingredient to copyright protection. It provides a solution to illegal copying of digital material and has many other useful applications such as broadcast monitoring and the recording of electronic transactions. Now, for the first time, there is a book that focuses exclusively on this exciting technology. Digital Watermarking covers the crucial research findings in the field: it explains the principles underlying digital watermarking technologies, describes the requirements that have given rise to them, and discusses the diverse ends to which these technologies are being applied. As a result, additional groundwork is laid for future developments in this field, helping the reader understand and anticipate new approaches and applications.

Digital Watermarking

Reversible data embedding has drawn lots of interest recently Being reversible, the original digital content can be completely restored We present a novel reversible data-embedding method for digital images We explore the redundancy in digital images to achieve very high embedding capacity, and keep the distortion low

Reversible data embedding using a difference expansion

Information-hiding techniques have recently become important in a number of application areas. Digital audio, video, and pictures are increasingly furnished with distinguishing but imperceptible marks, which may contain a hidden copyright notice or serial number or even help to prevent unauthorized copying directly. Military communications systems make increasing use of traffic security techniques which, rather than merely concealing the content of a message using encryption, seek to conceal its sender, its receiver, or its very existence. Similar techniques are used in some mobile phone systems and schemes proposed for digital elections. Criminals try to use whatever traffic security properties are provided intentionally or otherwise in the available communications systems, and police forces try to restrict their use. However, many of the techniques proposed in this young and rapidly evolving field can trace their history back to antiquity, and many of them are surprisingly easy to circumvent. In this article, we try to give an overview of the field, of what we know, what works, what does not, and what are the interesting topics for research.

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Information hiding-a survey

A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

Reversible data hiding

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

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.

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Digital camera identification from sensor pattern noise

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 describe a reliable and accurate method for detecting least significant bit (LSB) nonsequential embedding in digital images. The secret message length is derived by inspecting the lossless capacity in the LSB and shifted LSB plane. An upper bound of 0.005 bits per pixel was experimentally determined for safe LSB embedding.

Detecting LSB steganography in color, and gray-scale images

One common drawback of virtually all current data embedding methods is the fact that the original image is inevitably distorted due to data embedding itself. This distortion typically cannot be removed completely due to quantization, bit-replacement, or truncation at the grayscales 0 and 255. Although the distortion is often quite small and perceptual models are used to minimize its visibility, the distortion may not be acceptable for medical imagery (for legal reasons) or for military images inspected under nonstandard viewing conditions (after enhancement or extreme zoom). In this paper, we introduce a new paradigm for data embedding in images (lossless data embedding) that has the property that the distortion due to embedding can be completely removed from the watermarked image after the embedded data has been extracted. We present lossless embedding methods for the uncompressed formats (BMP, TIFF) and for the JPEG format. We also show how the concept of lossless data embedding can be used as a powerful tool to achieve a variety of nontrivial tasks, including lossless authentication using fragile watermarks, steganalysis of LSB embedding, and distortion-free robust watermarking.

/pdf/lossless-data-embedding-new-paradigm-in-digital-watermarking-2p9jpmkjl7.pdf

Lossless data embedding--new paradigm in digital watermarking

A large number of commercial steganographic programs use the Least Significant Bit embedding (LSB) as the method of choice for message hiding in 24-bit, 8-bit color images, and grayscale images. It is commonly believed that changes to the LSBs of colors cannot be detected due to noise that is always present in digital images. In this paper, we describe a new very accurate and reliable method that can detect LSB embedding in randomly scattered pixels in both 24-bit color images and 8-bit grayscale or color images. It is based on our previous work on lossless data embedding [1]. By inspecting the differences in the number of regular and singular groups for the LSB and the "shifted LSB plane", we can reliably detect messages as short as 0.03bpp.

/pdf/reliable-detection-of-lsb-steganography-in-color-and-2cfnnificu.pdf

Miroslav Goljan

Papers

Digital camera identification from sensor pattern noise

Determining Image Origin and Integrity Using Sensor Noise

Detecting LSB steganography in color, and gray-scale images

Lossless data embedding--new paradigm in digital watermarking

Reliable detection of LSB steganography in color and grayscale images