Evolving decision tree rule based system for audio stego anomalies detection based on Hausdorff distance statistics

This work is motivated by the interest in forensics steganalysis which is aimed at detecting the presence of secret messages transmitted through a subliminal channel. A critical part of the steganalyser design depends on the choice of stego-sensitive features and an efficient machine learning paradigm. The goals of this paper are: (1) to demonstrate that the higher-order statistics of Hausdorff distance - a dissimilarity metric, offers potential discrimination ability for a clean and a stego audio and (2) to achieve promising classification accuracy by realizing the proposed steganalyser with evolving decision tree classifier. Stego sensitive feature selection process is imparted by the genetic algorithm (GA) component and the construction of the rule base is facilitated by the decision tree module. The objective function is designed to maximize the Precision and Recall measures of the classifier thereby enhancing the detection accuracy of the system with low-dimensional and informative features. An extensive experimental evaluation of the proposed system on a database containing 4800 clean and stego audio files (generated by using six different embedding schemes), with the family of six GA decision trees was conducted. The observations reported as 90%+ detection rate, a promising score for a blind steganalyser, show that the proposed scheme, with the Hausdorff distance statistics as features and the evolving decision tree as classifier, is a state-of-the-art steganalyser that outperforms many of the previous steganalytic methods.     

A novel steganalysis for TPVD steganographic method based on differences of pixel difference histogram

Tri-way Pixel Value Differencing (TPVD) steganographic method is a new modified version of another well-known method called PVD, which intents to increase embedding capacity and security of its successor by hiding secret bits in both vertical and diagonal edges of a cover image, in addition to the horizontal edges used in PVD. In this paper, it is shown that the histogram of difference values of a stego image under the TPVD algorithms is vulnerable to a particular statistical analysis. So, a new steganalytic measure named Growing Anomalies is introduced that its value has a linear relationship with secret message rate. It is shown empirically and theoretically that proposed steganalysis method based on this measure can estimate the amount of secret bits with a negligible error rate. The proposed steganalyser can classify test images as stego or cover with 97% accuracy when they contain more that 10% secret data. Implementation results indicate that proposed method can estimate secret message rate with an average accuracy of 95%.     

Simple and effective approach for construction of universal blind steganalyzer

The goal of universal blind steganalysis is to detect all known (already existing) and unknown (previously unseen) steganographic algorithms without knowledge of the exact stego algorithm used by the steganographer. However, a binary blind steganalyzer trained on cover images and stego images randomly selected from “known stego images” (i.e., stego images produced by multiple “known” stego methods with a mixture of payloads), may fail catastrophically on unknown stego methods although shows superior performance on known stego methods. Additionally, unsupervised outlier detection and one-class classification approaches are less likely to fail to detect unknown stego methods but yield high false positive rates. Motivated by these observations, we explore a simple and effective approach for construction of universal blind steganalyzer to achieve overall good performance on both known and unknown stego algorithms. First, we compute Local Outlier Factor (LOF) scores of known stego sample points (feature vectors) with respect to test sample points. Then, we choose stego images with the lowest LOF scores from known stego images as training stego images. Finally, we train a binary classifier on cover images and chosen training stego images for test. Experimental results confirm that the proposed approach performs significantly better than the random sampling-based binary classification method, unsupervised outlier detection and one-class classification approaches on both known and unknown stego algorithms.     

基于一类支持向量机的隐秘图像盲检测算法

针对二类支持向量机分类器在图像密写分析应用中训练步骤复杂与推广性弱的缺点,提出了基于一类支持向量机分类器的真彩隐秘图像盲检测算法,算法选用小波包高阶统计特征,仅对正常图像训练建立分类器。实验表明,算法在检测系统效率和推广性方面有较好的表现。     

LSB steganographic payload location for JPEG-decompressed images

Steganalysis always concentrates on the detection of the existence of the hidden information embedded into some digital multimedia by steganography. This paper presents a novel steganographic payload location method for spatial least significant bit (LSB) steganography with the JPEG-decompressed images used as the cover images. Firstly, we discuss the possibility of recovering the original cover image and compute the probability of successfully recovering. Then, we compute the distributions of the difference values between the estimated cover pixels and stego pixels in the payload-carrying and no-payload-carrying positions. Finally, combining with the theory of hypothesis test, we present a method to locate the payload-carrying pixels of JPEG-decompressed images, which is suitable for both LSB replacement and LSB matching. The experimental results show that: 1) the number of stego images needed to accurately locate all the payload-carrying pixels in the proposed method approaches the lower bound; 2) given the number of stego images, in the proposed method, the number of the payload-carrying pixels precisely located approaches the upper bound; 3) the proposed method performs better than some current state-of-the-art steganographic payload location methods for JPEG-decompressed images.     

A steganography embedding method based on edge identification and XOR coding

In this paper, we present a novel image steganography algorithm that combines the strengths of edge detection and XOR coding, to conceal a secret message either in the spatial domain or an Integer Wavelet Transform (IWT) based transform domain of the cover image. Edge detection enables the identification of sharp edges in the cover image that when embedding in would cause less degradation to the image quality compared to embedding in a pre-specified set of pixels that do not differentiate between sharp and smooth areas. This is motivated by the fact that the human visual system (HVS) is less sensitive to changes in sharp contrast areas compared to uniform areas of the image. The edge detection method presented here is capable of estimating the exact edge intensities for both the cover and stego images (before and after embedding the message), which is essential when extracting the message. The XOR coding, on the other hand, is a simple, yet effective, process that helps in reducing differences between the cover and stego images. In order to embed three secret message bits, the algorithm requires four bits of the cover image, but due to the coding mechanism, no more than two of the four bits will be changed when producing the stego image. The proposed method utilizes the sharpest regions of the image first and then gradually moves to the less sharp regions. Experimental results demonstrate that the proposed method has achieved better imperceptibility results than other popular steganography methods. Furthermore, when applying a textural feature steganalytic algorithm to differentiate between cover and stego images produced using various embedding rates, the proposed method maintained a good level of security compared to other steganography methods.     

Sharing secrets in stego images with authentication

Recently, Lin and Tsai and Yang et al. proposed secret image sharing schemes with steganography and authentication, which divide a secret image into the shadows and embed the produced shadows in the cover images to form the stego images so as to be transmitted to authorized recipients securely. In addition, these schemes also involve their authentication mechanisms to verify the integrity of the stego images such that the secret image can be restored correctly. Unfortunately, these schemes still have two shortcomings. One is that the weak authentication cannot well protect the integrity of the stego images, so the secret image cannot be recovered completely. The other shortcoming is that the visual quality of the stego images is not good enough. To overcome such drawbacks, in this paper, we propose a novel secret image sharing scheme combining steganography and authentication based on Chinese remainder theorem (CRT). The proposed scheme not only improves the authentication ability but also enhances the visual quality of the stego images. The experimental results show that the proposed scheme is superior to the previously existing methods.     

A feature-based classification technique for blind image steganalysis

In contrast to steganography, steganalysis is focused on detecting (the main goal of this research), tracking, extracting, and modifying secret messages transmitted through a covert channel. In this paper, a feature classification technique, based on the analysis of two statistical properties in the spatial and DCT domains, is proposed to blindly (i.e., without knowledge of the steganographic schemes) to determine the existence of hidden messages in an image. To be effective in class separation, the nonlinear neural classifier was adopted. For evaluation, a database composed of 2088 plain and stego images (generated by using six different embedding schemes) was established. Based on this database, extensive experiments were conducted to prove the feasibility and diversity of our proposed system. It was found that the proposed system consists of: 1) a 90%/sup +/ positive-detection rate; 2) not limited to the detection of a particular steganographic scheme; 3) capable of detecting stego images with an embedding rate as low as 0.01 bpp; and 4) considering the test of plain images incurred low-pass filtering, sharpening, and JPEG compression.     

Secret image sharing based on cellular automata and steganography

Recently Lin and Tsai [Secret image sharing with steganography and authentication, The Journal of Systems and Software 73 (2004) 405-414] and Yang et al. [Improvements of image sharing with steganography and authentication, The Journal of Systems and Software 80 (2007) 1070-1076] proposed secret image sharing schemes combining steganography and authentication based on Shamir's polynomials. The schemes divide a secret image into some shadows which are then embedded in cover images in order to produce stego images for distributing among participants. To achieve better authentication ability Chang et al. [Sharing secrets in stego images with authentication, Pattern Recognition 41 (2008) 3130-3137] proposed in 2008 an improved scheme which enhances the visual quality of the stego images as well and the probability of successful verification for a fake stego block is 1/16.In this paper, we employ linear cellular automata, digital signatures, and hash functions to propose a novel (t,n)-threshold image sharing scheme with steganographic properties in which a double authentication mechanism is introduced which can detect tampering with probability 255/256. Employing cellular automata instead of Shamir's polynomials not only improves computational complexity from to O(n) but obviates the need to modify pixels of cover images unnecessarily. Compared to previous methods [C. Lin, W. Tsai, Secret image sharing with steganography and authentication, The Journal of Systems and Software 73 (2004) 405-414; C. Yang, T. Chen, K. Yu, C. Wang, Improvements of image sharing with steganography and authentication, The Journal of Systems and Software 80 (2007) 1070-1076; C. Chang, Y. Hsieh, C. Lin, Sharing secrets in stego images with authentication, Pattern Recognition 41 (2008) 3130-3137], we use fewer number of bits in each pixel of cover images for embedding data so that a better visual quality is guaranteed. We further present some experimental results.     

基于嵌入影响变化率的灰度图像LSB匹配检测方法

对灰度图像LSB匹配隐写提出了一种新的检测方法.通过引入不同幅度的修改,分析了消息嵌入过程对载体和载密图像影响的差异,并利用这种嵌入影响的变化率构造了一个分类器.实验表明该方法对于经过JPEG转换的灰度图像和非压缩灰度图像均有较好的检测效果.     

Sharing and hiding secret images with size constraint

This paper presents a method for sharing and hiding secret images. The method is modified from the (t,n) threshold scheme. (Comput.Graph. 26(5)(2002)765) The given secret image is shared and n shadow images are thus generated. Each shadow image is hidden in an ordinary image so as not to attract an attacker's attention. Any t of the n hidden shadows can be used to recover the secret image. The size of each stego image (in which a shadow image is hidden) is about 1/t of that of the secret image, avoiding the need for much storage space and transmission time (in the sense that the total size of t stego images is about the size of the secret image). Experimental results indicate that the qualities of both the recovered secret image and the stego images that contain the hidden shadows are acceptable. The photographers who work in enemy areas can use this system to transmit photographs.     

基于少样本学习的通用隐写分析方法

近年来,深度学习在图像隐写分析任务中表现出了优越的性能.目前,大多数基于深度学习的图像隐写分析模型为专用型隐写分析模型,只适用于特定的某种隐写术.使用专用隐写分析模型对其他隐写算法的隐写图像进行检测,则需要该隐写算法的大量载密图像作为数据集对模型进行重新训练.但在实际的通用隐写分析任务中,隐写算法的大量载密图像数据集是难以得到的.如何在极少隐写图像样本的情况下训练通用隐写分析模型是一个极大的挑战.对此,受少样本学习领域研究成果的启发,提出了基于转导传播网络的通用隐写分析方法.首先,在已有的少样本学习分类框架上改进了特征提取部分,设计了多尺度特征融合网络,使少样本分类模型能够提取到更多的隐写分析特征,使其可用于基于秘密噪声残差等弱信息的分类任务;其次,针对少样本隐写分析模型难收敛的问题,提出了预训练初始化的方式得到具有先验知识的初始模型;然后,分别训练了频域和空域的少样本通用隐写分析模型,通过自测和交叉测试,结果表明,检测平均准确率在80%以上;接着,在此基础上,采用数据集增强的方式重新训练了频域、空域少样本通用隐写分析模型,使少样本通用隐写分析模型检测准确率与之前相比提高到87%以上;...     

B样条驱动纹理生成的构造式信息隐藏

目的 传统构造式图像信息隐藏算法通常直接将图像空域特征与秘密信息关联,对算法的安全性造成威胁。因此,本文将曲线绘制函数与信息隐藏相结合,提出一种以B样条控制点为特征,在图像空域间接隐藏信息的算法。方法 算法主要分为信息隐藏及信息提取两阶段。在信息隐藏阶段,发送方首先通过选取初始控制点、仿射变换及B样条曲线绘制生成多条参考曲线,然后利用曲线控制点的位置隐藏信息,最后为图像填充颜色,即完成含密纹理图像的构造。在信息提取阶段,提取方根据纹理曲线和图像颜色获得含密曲线及参考曲线,经对照计算即可提取出秘密信息。结果 本算法具有较高的隐藏容量、鲁棒性和安全性。实验结果表明,由本文算法生成的800×800像素图像,其最高隐藏容量可达2870bits,分别是另两种典型构造式信息隐藏算法的6.7和3.4倍,且在质量因子为10的JPEG（joint photographic experts group）压缩攻击下的提取误码率可低至0,优于鲁棒较强的选择式信息隐藏算法LDA-DCT（robust coverless image steganography based on DCT and LDA topic classification）以及与之类似的构造式信息隐藏算法。同时,抗隐写分析检测实验表明,在隐藏容量小于250bits时检测误差趋近于0.5。结论 本文以B样条曲线控制点为特征,在纹理图像的绘制过程中隐藏信息,有效提高了传统构造式图像信息隐藏算法的安全性、隐藏容量和鲁棒性。     

RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

In the field of agriculture, the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants. This research focuses on identifying the plant diseases and detecting them promptly through the advancements in the field of computer vision. The images obtained from in-field farms are typically with less visual information. However, there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images. We propose a novel Reconstructed Disease Aware–Convolutional Neural Network (RDA-CNN), inspired by recent CNN architectures, that integrates image super resolution and classification into a single model for rice plant disease classification. This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots, rot, and lesion on different parts of the rice plants. Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other conventional Super Resolution (SR) methods. Furthermore, these super-resolution images are subsequently passed through deep classification layers for disease classification. The results demonstrate that the RDA- CNN significantly boosts the classification performance by nearly 4–6% compared with the baseline architectures.     

无损载体和鲁棒代价结合的JPEG图像鲁棒隐写

目的传统隐写技术在实际社交网络信道上难以保护秘密信息的完整性。在社交网络中,图像往往经过有损压缩信道进行传输,从而导致隐蔽通信失效。为了保证经过压缩信道传输的载密图像鲁棒性,设计安全鲁棒的隐蔽通信技术具有实际应用价值。基于最小化图像信息损失,本文提出无损载体和鲁棒代价结合的JPEG图像鲁棒隐写。方法首先,指出构造无损载体能有效维持隐写安全性和鲁棒性的平衡,对经过压缩信道前后的JPEG图像空域像素块进行差分,构造无损载体以确定鲁棒嵌入域;其次,通过对离散余弦变换(discrete cosine transform, DCT)系数进行"±1"操作,并计算空域信息在压缩传输前后的损失,设计衡量DCT系数抗压缩性能的鲁棒代价;同时,验证在低质量因子压缩信道下鲁棒代价更能区分DCT系数的鲁棒能力,最后,利用校验子格编码(syndrome-trellis code, STC),结合无损载体和鲁棒代价对秘密信息进行嵌入。结果实验在BossBase1.01图像库上进行对比实验,相比于传统JPEG隐写技术,构造无损载体作为嵌入域能有效地将信息平均提取错误率降低24.97%,图像的正确提取成功率提高了21...     

Blind image steganalysis based on content independent statistical measures maximizing the specificity and sensitivity of the system

This paper reports the design principles and evaluation results of a new experimental universal, blind image steganalysing system. This system investigates the use of content independent statistical evidences left by the steganograms, as features for an image steganalyzer. The work is aimed at maximizing the sensitivity and specificity of the steganalyzer and to accomplish both security and system performance. A genetic-X-means classifier is constructed to realize the proposed model. For performance evaluation, a database composed of 5600 plain and stego images (generated by using seven different embedding schemes) was established. The results of our empirical experiment prove the vitality of the proposed scheme in detecting stego anomalies in images. In addition, the simulation results show that the effectiveness of steganalytic system can be enhanced by considering the content independent distortion measures and maximizing the sensitivity and specificity of the system.     

LSB based non blind predictive edge adaptive image steganography

Image steganography is the art of hiding secret message in grayscale or color images. Easy detection of secret message for any state-of-art image steganography can break the stego system. To prevent the breakdown of the stego system data is embedded in the selected area of an image which reduces the probability of detection. Most of the existing adaptive image steganography techniques achieve low embedding capacity. In this paper a high capacity Predictive Edge Adaptive image steganography technique is proposed where selective area of cover image is predicted using Modified Median Edge Detector (MMED) predictor to embed the binary payload (data). The cover image used to embed the payload is a grayscale image. Experimental results show that the proposed scheme achieves better embedding capacity with minimum level of distortion and higher level of security. The proposed scheme is compared with the existing image steganography schemes. Results show that the proposed scheme achieves better embedding rate with lower level of distortion.     

发送方可否认图像隐写

目的 图像隐写是指将秘密信息隐藏到载体图像中,生成含密图像并在公共信道中传输。隐写分析旨在识别图像中是否隐藏秘密信息。不论何种隐写方案,都会在一定程度上被隐写分析识破,从而导致胁迫攻击,即攻击者找到发送方或接收方,胁迫其提交经过验证的秘密信息。为了保护秘密信息的隐蔽通信,对抗胁迫攻击的可否认方案亟待研究。在密码学领域,为了对抗胁迫攻击,已经提出了可否认加密的概念及相关方案并受到关注与研究。而在隐写领域,有研究提出可否认隐写的概念并设计了接收方可否认隐写的框架,但没有发送方可否认隐写的相关研究。对此,本文讨论发送方可否认隐写。方法 设计方案的通用框架,并构造两个方案:基于可逆网络的发送方可否认图像隐藏方案和基于可否认加密的发送方可否认图像隐写方案。在发送方可否认隐写的框架下,发送方可使用虚假的秘密信息生成与攻击者手中相同的含密图像,以欺骗攻击者,逃脱胁迫攻击,保护真实的秘密信息。结果 实验结果表明,两个方案都是可行且有效的,与原始隐写方案相比,可否认方案达到了发送方可否认功能的同时,均不会显著降低含密图像的视觉质量(峰值信噪比(peak signal-to-noise ratio,PSN...     

Deep residual learning for image steganalysis

Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.     

Hilbert quantum image scrambling and graph signal processing-based image steganography

Steganography plays a big role in secret communication by concealing secret information in the carrier. This paper presents a graph signal processing-based robust image steganography technique for posting images over social networks. In the embedding, we first obtained a scrambled version of the secret image using quantum scrambling. Next, we applied graph wavelet transformation on both the cover image and scrambled secret image followed by α (alpha) blending on both image signals (cover image signal and scrambled image signal). Finally, inverse graph wavelet transformation of the resulting image was undertaken to obtain the stego image. In this paper, the use of graph wavelet transformation improved interpixel correlation, which resulted in the excellent visual quality of both the stego image and the extracted secret image. Our experiments show that the picture quality of both the cover image and the stego image is exactly the same.