Advances in Multimedia

Volume 2016, Article ID 6714164, 10 pages

http://dx.doi.org/10.1155/2016/6714164

## Image Encryption Performance Evaluation Based on Poker Test

School of Information Engineering, Chang’an University, Middle Section of Nan’er Huan Road, Xi’an 710064, China

Received 26 March 2016; Revised 24 May 2016; Accepted 5 June 2016

Academic Editor: Deepu Rajan

Copyright © 2016 Shanshan Li and Weiyang Sun. 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 fast development of image encryption requires performance evaluation metrics. Traditional metrics like entropy do not consider the correlation between local pixel and its neighborhood. These metrics cannot estimate encryption based on image pixel coordinate permutation. A novel effectiveness evaluation metric is proposed in this paper to address the issue. The cipher text image is transformed to bit stream. Then, Poker Test is implemented. The proposed metric considers the neighbor correlations of image by neighborhood selection and clip scan. The randomness of the cipher text image is tested by calculating the chi-square test value. Experiment results verify the efficiency of the proposed metrics.

#### 1. Introduction

The accelerating growth of personal smart devices and Internet makes it easy to distribute, share, and exchange digital image data via various sorts of open networks. It is simple to access these image data when they are transmitted via open networks. In this case, image data security has become a crucial issue because some of the image content needs to be kept confidential. Image encryption is an effective solution to guarantee image data security. The encryption process converts the original image into another incomprehensible image. The ideal cipher text image is not intelligible. Such a cipher text image could be stored or transmitted across insecure networks without content leaking to anyone except the intended recipient. Since images have certain characteristics such as bulk data size and high intercorrelation, image encryption schemes focus on destroying the correlation between neighbor pixels. This requires that cipher text image should appear as meaningless noises.

Since image encryption has attracted extensive attention, various encryption schemes have been proposed in recent years. As a result, efficient image encryption performance evaluation is desired. To evaluate the encryption performance helps optimizing parameter setting as well as improving encryption scheme [1]. Subjective preference test provides several cipher text images and asks the observers to find out the one that they consider to be best performed. The subjective test is intuitive and straightforward. However, it is inconvenient, time-consuming, and expensive. Hence, it has led to a rising demand for objective evaluations. Image encryption destroys the correlation between neighbor pixels. Cipher text image should be nonrelative to the original plain text image and appear like meaningless noise. As a result, there are two kinds of objective evaluation metrics. The first kind tries to estimate the relation between cipher text image and plain text image. Correlation coefficient is a metric of this kind, which calculates the correlation coefficient between pixels in the same location in the plain text and cipher text images [2]. Another widely used metric is the deviation from identity, which measures the deviation of the histogram of the encrypted image from the histogram of the ideally encrypted image [3]. This kind of objective metrics requires the knowledge of reference image. The other kind focuses on randomness test of cipher text image. This kind of objective metrics does not need the plain text image as reference [4]. The nonreference objective encryption performance evaluation is more practical because the reference image is not always available. Several statistical tests for randomness could be employed to evaluate image encryption performance, such as approximate entropy and block frequency [5]. These evaluations usually require transformation of image to bit stream.

This paper proposes a nonreference objective metric to evaluate the image encryption performance. The novel metric is based on Poker Test with consideration of pixel neighborhood relationships. First traditional objective criterions are discussed. Then, Poker Test is introduced. The novel metric is described. Finally, performance of the metric is evaluated to check the effectiveness in Section 5.

#### 2. Traditional Metrics

When an image encryption scheme is proposed, several performance analyses will be implemented to estimate the effectiveness of novel encryption scheme. The most widely used evaluation criterions include encryption quality and Shannon entropy.

##### 2.1. Encryption Quality

The encryption quality is designed to measure the change rate of pixel values when encryption is applied to an image [6]. Higher change in pixel value indicates the image encryption and the encryption quality to be more effective. The encryption quality is expressed in terms of total changes in pixels values between the plain text image and the cipher text image.

Let and denote the plain text image and cipher text image, respectively. Assume and are the numbers of occurrences for each gray level in the plain text image and cipher text image; the total gray level is . Encryption quality is defined as follows:

The encryption quality could not estimate encryption based on image pixel coordinate permutation. The pixel positions shuffle without values changing makes no difference between and . In this case, encryption quality could not evaluate the performance of encryption. Moreover, encryption quality requires the knowledge of plain text image , which is not always available.

##### 2.2. Shannon Entropy

Shannon entropy or information entropy is a measurement of uncertainty. The greater value of entropy indicates the system is more random. When a random variable’s probability distributes equally, the Shannon entropy will be the biggest. If the Shannon entropy of a cipher text image approaches the theoretical peak value, the encryption will be considered as effective [7].

Assume the probability of pixel value is and the total gray level is ; Shannon entropy of the image is calculated byShannon entropy requires no reference image. However, it cannot evaluate the encryption based on pixel position shuffle either. The neighbor correlations of pixels are not considered.

##### 2.3. Autocorrelation

Autocorrelation is the cross-correlation of a signal with itself at different points in time [8]. Pixels in meaningful image are correlative in horizontal, vertical, and diagonal directions. Effective image encryption destroys the correlation among adjacent pixels. Thus, two-dimensional autocorrelation values could be adopted to evaluate the performance of image encryption. As the correlation between adjacent pixels is the focus, normalized autocorrelation values of one pixel shifted on these three directions are employed as the measurement of image encryption quality. That means the center of the mask image is shifted from the center of original image with one pixel. The normalized 2D autocorrelation of image is defined asin which is the pixel value in image at position . is the mean value of the image. If the size of the image is , will be a matrix with size of . The normalized autocorrelation value with one-pixel shift is the value in matrix at position of , , and

##### 2.4. Gap Test

The gap test is used for testing randomness of a sequence. It is concerned with the number of gaps in any particular class of digits [9]. The elements of the sequence are classified into two categories: the elements with values between zero and upper bound, marked as 0, and the elements with other values, marked as 1. The continuous zeros are defined as “gap.” The length of zero gaps is counted. Then the chi-square statistic is calculated byin which is the number of zero gaps of length and is the probability of the length of zero gaps equal to . is calculated by , where is the probability that the element belongs to category 0.

#### 3. Poker Test

Poker Test is a randomness test. It treats numbers grouped together as a poker’s hand. The hands obtained are compared to what is expected. The classical Poker Test consists of using all possible categories obtained from poker that uses hands of five numbers. In practice, Poker Test can be applied without being restricted to hands of five numbers. For cryptography application, four numbers are more convenient to deal with bit streams [10].

National Institute of Standards and Technology designed randomness test FIP 140-2 with Poker Test as the second test [11]. The Poker Test in FIP 140-2 is defined as follows: a single bit stream of 20,000 consecutive bits shall be divided into 5,000 nonoverlapping parts. These parts are called nibbles [12]. One nibble consists of 4 bits. The numbers of occurrences of each of the 2^{4} = 16 possible values are counted and stored. The Poker Test determines whether these nibbles appear approximately in the same frequency. The chi-square test is employed to evaluate the randomness by formulain which is the number of the th possible values. It is obvious that Poker Test deals with bit stream and the number of the streams is constrained. A cipher image is a two-dimensional matrix with unknown size and integer values of elements. These issues require being addressed if we try to evaluate the randomness of cipher text image by the Poker Test.

#### 4. Novel Metric

Image encryption destroys the correlation between neighbor pixels. Cipher text image should appear like meaningless noise as much as possible. Effective image encryption should generate randomness like cipher text image. The novel encryption performance evaluation metric is based on randomness test of cipher text image and pixel neighborhood correlations. Figure 1 provides the flowchart of encryption performance evaluation by proposed novel metric.