Security and Communication Networks

Volume 2018, Article ID 4313769, 11 pages

https://doi.org/10.1155/2018/4313769

## A VQ-Based Joint Fingerprinting and Decryption Scheme for Secure and Efficient Image Distribution

^{1}College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China^{2}College of Computer Science, Chongqing University, Chongqing 400030, China

Correspondence should be addressed to Ming Li; nc.ude.uth@gnimil

Received 19 February 2018; Revised 26 April 2018; Accepted 13 June 2018; Published 6 August 2018

Academic Editor: Emanuele Maiorana

Copyright © 2018 Ming Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The first joint fingerprinting and decryption (JFD) for vector quantization (VQ) images addressed the problem that the decrypted multimedia data may be redistributed from authorized customers to unauthorized customers. The scheme also caused conventional JFD methods to be equipped with a special ability to resist noise interference. Till now, some existing schemes related have been proposed to protect the multimedia content and distribution, but these schemes failed to tackle several problems existing in the original JFD scheme based on VQ image, including high transmission cost and severe fingerprinted image distortion. In this paper, we propose a novel JFD method by combining a weight-sum function with fingerprinting embedding and extraction for VQ images. Under the combination, the visual quality of the fingerprinted image is further improved; also the fingerprint extraction implements a blind extraction process. Experiments and analyses demonstrate the feasibility of the proposed method.

#### 1. Introduction

The digitized form of numerous multimedia contents not only facilities various operations but also allows fast distribution from one physical location to another via the Internet. As any advancement in technique can always be used in good and bad ways, the convenience of technique simultaneously incurs the potential security hazard when illegally redistributing the protected content via the Internet. To improve the security of multimedia contents in transmission and distribution, digital fingerprint has been widely investigated over the past few years [1–4]. Fingerprint is the technique that is utilized to trace the traitors to ensure the reasonable distribution of the protected content; in this technique a fingerprint sequence only represents a unique user’s identity and is usually embedded in such a way that remains invisible to the human eye. Conventional fingerprint embedding can be achieved in the spatial domain [5, 6] or in the transform domain [2, 7]. Once the fingerprinted content is illegally redistributed, the fingerprint sequence is required for extracting and checking the user’s identity.

Specifically, fingerprint embedded into host image is especially designed in various application scenarios. In the transmitter-side fingerprinting application [8, 9], such fingerprint is embedded on the transmitted side and then the fingerprinted contents are encrypted and sent to users via multiple unicast transmission. For the network-based fingerprinting application [10], this type of fingerprint is embedded as the data travels through the network with special network devices. The last type of fingerprint, i.e., the receiver-side fingerprint, which is the most promising method in the fingerprint research field, has been widely applied in some scenarios where the pirate possibly redistributes his or her copies over the Internet. Combining receiver-side fingerprint and image decryption operation can effectively trace the owners of illegal redistribution contents, which is the well-known traitor tracing problem.

The first receiver-side fingerprinting method [11] was introduced by processing decryption and fingerprinting independently. The respective operations of decryption and fingerprinting embedding made it possible for the attackers to intercept the decrypted content and illegally redistribute it via the Internet. To decrease the possibility of content leakage, some novel joint fingerprint and decryption (JFD) schemes were produced in [7, 12, 13], which ensure the fingerprint performed during decryption process. Although these schemes effectively resolve the problem of illegal distribution at the receiver end, they cannot be applied to the VQ (vector quantization) domain and the encrypted image cannot effectively resist noise interference. To solve this problem, a new JFD scheme for VQ images was proposed by Lin et al. in 2012 [14]. In their method, the encryption process could be classified into two types: one was that the most well-known chaotic function and static-tree were employed in the permutation and codeword substitution processes, respectively. The other was to utilize the dynamic-tree and session key into the substitution process. Recently, a larger number of JFD schemes on the basis of Lin et al. method have been proposed [15–18]; in these schemes, the security of JFD methods was further enhanced and applied into different types of practical scenarios. Unfortunately, there still exist some problems to be solved for the original scheme [14].

In this paper, we improve the method of decryption and fingerprinting process of [14] through using index-block-based decryption method and a weight-sum function of [19]; namely, the weight-sum function is employed to embed (*n*+1) fingerprint bits into a block with 2^{n} codeword indices by modifying only one codeword index. More specifically, the key factor for JFD method based on VQ image is to achieve fingerprint embedding during decryption. A shortcoming of the original scheme [14] is that it only easily embeds each fingerprint bit into each codeword index but has limitations not only in terms of the flexibility of maximum fingerprint bits but also in terms of the quality of the fingerprinted image. Conversely, in the proposed scheme, the major works concentrate on the JFD process. We select a block-based fingerprint embedding to implement this process. The major difference between the proposed method and scheme [14] is the way of how to efficiently embed fingerprint into codeword index and ensure the changeable maximum fingerprint capacity. The major merits are summarized as follows:(1)The encrypted codeword indices are capable of being efficiently transmitted with much smaller bandwidth and cost.(2)The flexibility of fingerprint embedding can be ensured, and the changeable maximum fingerprint capacity can be achieved.(3)The fingerprint extraction operation in the traitor tracing process is a blind extraction process.(4)Comparing with the original scheme in [14], the number of modifiable blocks is obviously lowered when introducing certain user’s fingerprinting sequence. That is, given the same embedding rate, the PSNR of our method is higher than that in the original scheme.

#### 2. Preliminary

A brief overview of the concepts used in the proposed scheme is discussed as follows.

##### 2.1. Vector Quantization

Vector quantization (VQ) is the technique that converts original image blocks into specific codeword indices relying on the known codebook to implement the goal of compression, in which the design of codebook directly affects the compression efficiency and image restoration quality. It has a similar functionality with palette technique, the latter transforms color value into index value which can be found by color look-up table, and the maximum of indispensable color of an image directly determines the ultimate compression result. Generally, vector quantization (VQ) designed for image compression is composed of two parts: encoding and decoding processes. Encoding process is to divide original image into nonoverlapping blocks with size of *×h* and map the divided image blocks into a finite subset of codebooks (CB), where denotes the codebook and is the th codeword that comprises . For each vector of original image, its nearest codeword in the codebook can be found by using Euclidean distance.where and denote the th elements of vectors and , respectively. The generated nearest codeword is then exploited to decrypt the vector* X*, i.e., one block of divided image. Transforming the encrypted indices into VQ image is the decoding process which can be easily realized by table look-up.

##### 2.2. Principal Components Analysis

As [14] depicted, principal components analysis (PCA) algorithm has attracted considerable attention from various research fields, especially used in data and signal analysis [20, 21]. Here, the functionality of PCA is mainly used for sorting codewords of codebook before encryption, which is essential in the encryption and JFD phases. Given the unsorted codewords , , and , after applying PCA algorithm, the sorted codewords are denoted as , , and , where represents the size of codebook. The detailed process of PCA method can be found in [14].

##### 2.3. Weight-Sum Function

An essential starting point of designing weight-sum function is to conceal as much additional data as possible into cover image with modifying as little data as possible, and its fundamental objective is consistent with that of exploiting modification direction (EMD) [22]. Concretely, the methods of both are to conceal given bits into one block with modifying only a pixel; the former transforms (n+1) bits into decimal number under the size 2^{n} of one block to implement data hiding, but the latter requires converting additional data into (2*n* + 1)-ary notational system according to the size of a block to attain data embedding. A weight-sum function [19] is also a typical application for watermarking embedding, which is primarily used for the detection of the tempered areas. The main contributions of the weight-sum function in [19] can be summarized as follows. In the watermark embedding, the host image is first divided into blocks and each block contains 2^{n} pixels, and then (*n*+1) authentication bits need to be embedded into one block with modifying only a pixel. However, how to find the position of the modifiable pixel is a pressing problem to be solved, which is the functionality of the weight-sum function. Figure 1 shows the workflow of the weight-sum function, and one can easily know that the weight-sum function in Figure 1 directly implements the watermark embedding process.