In order to achieve high practical security, Natural Steganography (NS) uses cover images captured at ISO sensitivity $ISO_{1}$ and generates stego images mimicking ISO sensitivity $ISO_{2}>ISO_{1}$. This is achieved by adding a stego signal to the cover that mimics the sensor photonic noise. This paper proposes an embedding mechanism to perform NS in the JPEG domain after linear developments by explicitly computing the correlations between DCT coefficients before quantization. In order to compute the covariance matrix of the photonic noise in the DCT domain, we first develop the matrix representation of demosaicking, luminance averaging, pixel section, and 2D-DCT. A detailed analysis of the resulting covariance matrix is done in order to explain the origins of the correlations between the coefficients of $3\times3$ DCT blocks. An embedding scheme is then presented that takes in order to take into account all the correlations. It employs 4 sub-lattices and 64 lattices per sub-lattices. The modification probabilities of each DCT coefficient are then derived by computing conditional probabilities from the multivariate Gaussian distribution using the Cholesky decomposition of the covariance matrix. This derivation is also used to compute the embedding capacity of each image. Using a specific database called E1 Base, we show that in the JPEG domain NS (J-Cov-NS) enables to achieve high capacity (more than 2 bits per non-zero AC DCT) and with high practical security ($P_{\mathrm{E}}\simeq40\%$ using DCTR from QF 75 to QF 100).
A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) have been proposed and achieved success for spatial steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its anti-detectability and training efficiency should be improved. In conventional steganography, research has shown that the side-information calculated from the precover can be used to enhance security. Ho…
This short paper proposes to use the statistical analysis of the correlation between DCT coefficients to design a new synchronization strategy that can be used for cost-based steganographic schemes in the JPEG domain. First, an analysis is performed on the covariance matrix of DCT coefficients of neighboring blocks after a development similar to the one used to generate BossBase. This analysis exhibits groups of uncorrelated coefficients: 4 groups per block and 2 groups of uncorrelated diagonal neighbors together with groups of mutually correlated coefficients groups of 6 coefficients per bloc…
Convolutional Neural Networks (CNN) based methods have significantly improved the performance of image steganalysis compared with conventional ones based on hand-crafted features. However, many existing literatures on computer vision have pointed out that those effective CNN-based methods can be easily fooled by adversarial examples. In this paper, we propose a novel steganography framework based on adversarial example in an iterative manner. The proposed framework first starts from an existing embedding cost, such as J-UNIWARD in this work, and then updates the cost iteratively based on adver…
Recently, with the introduction of JPEG phase-aware steganalysis features, e.g., GFR, the design of JPEG steganographic distortion cost function turns to maintain not only the statistical undetectability in DCT domain but also in spatial domain. To tackle this issue, this paper presents a novel paradigm for the design of JPEG steganographic distortion cost function, which calculates the distortion cost via a generalized Distortion Cost Domain Transformation (DCDT) function. The proposed function comprises the decompressed pixel block embedding changes and their corresponding embedding distorti…
By cross-coupling two logistic maps a novel method is proposed for the public key steganography in JPEG image. Chaotic maps entail high complexity in the used algorithm for embedding secret data in a medium. In this paper, discrete cross- coupled chaotic maps are used to specifying the location of the different parts of the secret data in the image. Modifying JPEG format during compressing and decompressing, and also using public key enhanced difficulty of the algorithm. Simulation results show that in addition to excessive capacity, this method has high robustness and resistance against hacke…
Because Facebook is available on hundreds of millions of desktop and mobile computing platforms around the world and because it is available on many different kinds of platforms (from desktops and laptops running Windows, Unix, or OS X to hand held devices running iOS, Android, or Windows Phone), it would seem to be the perfect place to conduct steganography. On Facebook, information hidden in image files will be further obscured within the millions of pictures and other images posted and transmitted daily. Facebook is known to alter and compress uploaded images so they use minimum space and b…
Traditional adaptive steganography is a technique used for covert communication with high security, but it is invalid in the case of stego images are sent to legal receivers over networks which is lossy, such as JPEG compression of channels. To deal with such problem, robust adaptive steganography is proposed to enable the receiver to extract secret messages from the damaged stego images. Previous works utilize reverse engineering and compression-resistant domain constructing to implement robust adaptive steganography. In this paper, we adopt modification with re-compression scheme to improve …
It is well known that the designing or improving embedding cost becomes a key issue for current steganographic methods. Unlike existing works, we propose a novel framework to enhance the steganography security via post-processing on the embedding units (i.e., pixel values and DCT coefficients) of stego directly. In this paper, we firstly analyze the characteristics of STCs (Syndrome-Trellis Codes), and then design the rule for post-processing to ensure the correct extraction of hidden message. Since the steganography artifacts are typically reflected on image residuals, we try to reduce the re…
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；College of Information Engineering, Shenzhen University（深圳大学信息工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Alibaba Group（阿里巴巴集团）
Recent research has shown that non-additive image steganographic frameworks effectively improve security performance through adjusting distortion distribution. However, as far as we know, all of the existing non-additive proposals are based on handcrafted policies, and can only be applied to a specific image domain, which heavily prevent non-additive steganography from releasing its full potentiality. In this paper, we propose an automatic non-additive steganographic distortion learning framework called MCTSteg to remove the above restrictions. Guided by the reinforcement learning paradigm, we…
In this paper, we present a new reference dataset simulating digital evidence for image steganography. Steganography detection is a digital image forensic topic that is relatively unknown in practical forensics, although stego app use in the wild is on the rise. This paper introduces the first database consisting of mobile phone photographs and stego images produced from mobile stego apps, including a rich set of side information, offering simulated digital evidence. StegoAppDB, a steganography apps forensics image database, contains over 810,000 innocent and stego images using a minimum of 10…
Conventional covert image communication is assumed to transmit the message, in the securest way possible for a given payload, over lossless channels, and the associated steganographic schemes are generally vulnerable to active attacks, e.g., JPEG re-compression, scaling, as seen on social networks. Although considerable progress has been made on robust steganography against JPEG re-compression, there exist few steganographic schemes capable of resisting to scaling attacks due to the tricky inverse interpolations involved in algorithm design. To tackle this issue, a framework for robust image s…
This paper presents a novel method for detection of LSB matching steganogra- phy in grayscale images. This method is based on the analysis of the differences between neighboring pixels before and after random data embedding. In natu- ral images, there is a strong correlation between adjacent pixels. This correla- tion is disturbed by LSB matching generating new types of correlations. The pre- sented method generates patterns from these correlations and analyzes their varia- tion when random data are hidden. The experiments performed for two different image databases show that the method yields…
National Institute of Informatics（国立信息学研究所）；Feng Chia University（逢甲大学）；University of Jinan（济南大学）；Fujian Polytechnic Normal University（福建技术师范学院）；University of Warwick（华威大学）；Deakin University（迪肯大学）
Deep learning is regarded as a promising solution for reversible steganography. There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of reversible steganography. This end-to-end paradigm, however, suffers from imperfect reversibility. By contrast, the modular paradigm that incorporates neural networks into modules of traditional pipelines can stably guarantee reversibility with mathematical explainability. Prediction-error modulation is a well-established reversible steganography pip…
We propose a de-identification pipeline that protects the privacy of humans in video sequences by replacing them with rendered 3D human models, hence concealing their identity while retaining the naturalness of the scene. The original images of humans are steganographically encoded in the carrier image, i.e. the image containing the original scene and the rendered 3D human models. We qualitatively explore the feasibility of our approach, utilizing the Kinect sensor and its libraries to detect and localize human joints. A 3D avatar is rendered into the scene using the obtained joint positions, …
With the rapid advancements in digital imaging systems and networking, low-cost hand-held image capture devices equipped with network connectivity are becoming ubiquitous. This ease of digital image capture and sharing is also accompanied by widespread usage of user-friendly image editing software. Thus, we are in an era where digital images can be very easily used for the massive spread of false information and their integrity need to be seriously questioned. Application of multiple lossy compressions on images is an essential part of any image editing pipeline involving lossy compressed imag…
Tackle CSM in JPEG Steganalysis with Data Adaptation
LABEL4.AI；Univ. Lille（里尔大学）；CNRS（国家科学研究中心）；Centrale Lille（里尔中央理工大学）；UMR 9189 CRIStAL（里尔大学UMR 9189 CRIStAL）；IMT Nord Europe（北欧IMT）；Centre for Digital System（数字系统中心）；Department of Computers（计算机系）
The design of the optimal inverse discrete cosine transform (IDCT) to compensate the quantization error is proposed for effective lossy image compression in this work. The forward and inverse DCTs are designed in pair in current image/video coding standards without taking the quantization effect into account. Yet, the distribution of quantized DCT coefficients deviate from that of original DCT coefficients. This is particularly obvious when the quality factor of JPEG compressed images is small. To address this problem, we first use a set of training images to learn the compound effect of forwa…
