Modern Graph Theory

Each copy of a text document can be made different in a nearly invisible way by repositioning or modifying the appearance of different elements of text, i.e., lines, words, or characters. A unique copy can be registered with its recipient, so that subsequent unauthorized copies that are retrieved can be traced back to the original owner. In this paper we describe and compare several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion. The marks are intended to protect documents of limited value that are owned by individuals who would rather possess a legal than an illegal copy if they can be distinguished. We will describe attacks that remove the marks and countermeasures to those attacks. An architecture is described for distributing a large number of copies without burdening the publisher with creating and transmitting the unique documents. The architecture also allows the publisher to determine the identity of a recipient who has illegally redistributed the document, without compromising the privacy of individuals who are not operating illegally. Two experimental systems are described. One was used to distribute an issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, and the second was used to mark copies of company private memoranda.

Copyright protection for the electronic distribution of text documents : Identification and protection of multimedia information

A theory of overall quantization noise for nonsubtractive dither was originally developed in unpublished work by J.N. Wright and by T.J. Stockham and subsequently expanded by L.K. Brinton, S.P. Lipshitz, J. Vanderkooy, and R.A. Wannamaker. It is suggested that since these latter results are not as well known as the original results, misunderstanding persists in the literature. New proofs of the properties of quantizer dither, both subtractive and nonsubtractive, are provided. The new proofs are based on elementary Fourier series and Rice's characteristic function method and do not require the use of generalized functions (impulse trains of Dirac delta functions) and sampling theorem arguments. The goal is to provide a unified derivation and presentation of the two forms of dithered quantizer noise based on elementary Fourier techniques. >

http://ieeexplore.ieee.org/iel4/557/4025/00695075.pdf

Dithered Quantizers

Similar code may exist in large software projects due to some common software engineering practices, such as copying and pasting code and n-version programming Although previous work has studied syntactic equivalence and small-scale, coarse-grained program-level and function-level semantic equivalence, it is not known whether significant fine-grained, code-level semantic duplications exist Detecting such semantic equivalence is also desirable because it can enable many applications such as code understanding, maintenance, and optimizationIn this paper, we introduce the first algorithm to automatically mine functionally equivalent code fragments of arbitrary size - down to an executable statement Our notion of functional equivalence is based on input and output behavior Inspired by Schwartz's randomized polynomial identity testing, we develop our core algorithm using automated random testing: (1) candidate code fragments are automatically extracted from the input program; and (2) random inputs are generated to partition the code fragments based on their output values on the generated inputs We implemented the algorithm and conducted a large-scale empirical evaluation of it on the Linux kernel 2624 Our results show that there exist many functionally equivalent code fragments that are syntactically different (ie, they are unlikely due to copying and pasting code) The algorithm also scales to million-line programs; it was able to analyze the Linux kernel with several days of parallel processing

/pdf/automatic-mining-of-functionally-equivalent-code-fragments-1xuhqmhj8n.pdf

Automatic mining of functionally equivalent code fragments via random testing

In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.

Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images

A reversible (also called lossless, distortion-free, or invertible) visible watermarking scheme is proposed to satisfy the applications, in which the visible watermark is expected to combat copyright piracy but can be removed to losslessly recover the original image. We transparently reveal the watermark image by overlapping it on a user-specified region of the host image through adaptively adjusting the pixel values beneath the watermark, depending on the human visual system-based scaling factors. In order to achieve reversibility, a reconstruction/recovery packet, which is utilized to restore the watermarked area, is reversibly inserted into non-visibly-watermarked region. The packet is established according to the difference image between the original image and its approximate version instead of its visibly watermarked version so as to alleviate its overhead. For the generation of the approximation, we develop a simple prediction technique that makes use of the unaltered neighboring pixels as auxiliary information. The recovery packet is uniquely encoded before hiding so that the original watermark pattern can be reconstructed based on the encoded packet. In this way, the image recovery process is carried out without needing the availability of the watermark. In addition, our method adopts data compression for further reduction in the recovery packet size and improvement in embedding capacity. The experimental results demonstrate the superiority of the proposed scheme compared to the existing methods.

/pdf/a-contrast-sensitive-reversible-visible-image-watermarking-58dki5d9su.pdf

A Contrast-Sensitive Reversible Visible Image Watermarking Technique

We present a review of recent techniques for performing geometric analysis in cultural heritage CH applications. The survey is aimed at researchers in the areas of computer graphics, computer vision and CH computing, as well as to scholars and practitioners in the CH field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives.

/pdf/a-survey-of-geometric-analysis-in-cultural-heritage-30n5cg5e41.pdf

A Survey of Geometric Analysis in Cultural Heritage

We propose a simple yet efficient steganalytic algorithm for watermarks embedded by two state-of-the-art 3D watermarking algorithms by Cho et al. The main observation is that while in a clean model the means/variances of Cho et al.’s normalized histogram bins are expected to follow a Gaussian distribution, in a marked model their distribution will be bimodal. The proposed algorithm estimates the number of bins through an exhaustive search and then the presence of a watermark is decided by a tailor made normality test or a $t$ -test. We also propose a modification of Cho et al.’s watermarking algorithms with the watermark embedded by changing the histogram of the radial coordinates of the vertices. Rather than targeting a continuous statistics such as the mean or variance of the values in a bin, the proposed watermarking modifies a discrete statistic, which here is the height of the histogram bin, to achieve watermark embedding. Experimental results demonstrate that the modified algorithm offers not only better resistance against the steganalytic attack we developed, but also an improved robustness/capacity trade-off.

/pdf/a-3d-steganalytic-algorithm-and-steganalysis-resistant-336ri5q9oa.pdf

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking

A removable visible watermarking scheme, which operates in the discrete cosine transform (DCT) domain, is proposed for combating copyright piracy. First, the original watermark image is divided into 16×16 blocks and the preprocessed watermark to be embedded is generated by performing element-by-element matrix multiplication on the DCT coefficient matrix of each block and a key-based matrix. The intention of generating the preprocessed watermark is to guarantee the infeasibility of the illegal removal of the embedded watermark by the unauthorized users. Then, adaptive scaling and embedding factors are computed for each block of the host image and the preprocessed watermark according to the features of the corresponding blocks to better match the human visual system characteristics. Finally, the significant DCT coefficients of the preprocessed watermark are adaptively added to those of the host image to yield the watermarked image. The watermarking system is robust against compression to some extent. The performance of the proposed method is verified, and the test results show that the introduced scheme succeeds in preventing the embedded watermark from illegal removal. Moreover, experimental results demonstrate that legally recovered images can achieve superior visual effects, and peak signal-to-noise ratio values of these images are >50 dB.

Removable visible image watermarking algorithm in the discrete cosine transform domain

We propose a steganalytic algorithm for triangle meshes, based on the supervised training of a classifier by discriminative feature vectors. After a normalization step, the triangle mesh is calibrated by one step of Laplacian smoothing and then a feature vector is computed, encoding geometric information corresponding to vertices, edges and faces. For a given steganographic or watermarking algorithm, we create a training set containing unmarked meshes and meshes marked by that algorithm, and train a classifier using Quadratic Discriminant Analysis. The performance of the proposed method was evaluated on six well-known watermarking/steganographic schemes with satisfactory accuracy rates.

/pdf/mesh-discriminative-features-for-3d-steganalysis-3cqy1jhqdf.pdf

Ying Yang

Papers

A Contrast-Sensitive Reversible Visible Image Watermarking Technique

A Survey of Geometric Analysis in Cultural Heritage

A 3D Steganalytic Algorithm and Steganalysis-Resistant Watermarking

Removable visible image watermarking algorithm in the discrete cosine transform domain

Mesh Discriminative Features for 3D Steganalysis