This paper focuses on steganography based on pixel intensity value decomposition. A number of existing schemes such as binary, Fibonacci, Prime, Natural, Lucas, and Catalan-Fibonacci (CF) are evaluated in terms of payload capacity and stego quality. A new technique based on a specific representation is used to decompose pixel intensity values into 16 (virtual) bit-planes suitable for embedding purposes. The new decomposition scheme has a desirable property whereby the sum of all bit-planes does not exceed the maximum pixel intensity value, i.e. 255. Experimental results demonstrate that the new decomposition scheme offers a better compromise between payload capacity and stego quality than other existing decomposition schemes used for embedding messages. However, embedding in the 6th bit-plane onwards, the proposed scheme offers better stego quality. In general, the new decomposition technique has less effect in terms of quality on pixel value when compared to most existing pixel intensity value decomposition techniques when embedding messages in higher bit-planes.
Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego image. Steganography is commonly used for illegal purposes such as terrorist activities and pornography. To thwart covert communications and transactions, attacking algorithms against steganography, called steganalysis, exist. Currently, there are many studies implementing deep learning to the steganography algorithm. However, conventional steganalysis is no longer effective for deep learning based steganography algorit…
In most steganographic methods, increasing in the capacity leads to decrease in the quality of the stego-image, so in this paper, we propose to combine two existing techniques, Pixel value differencing and Gray Level Modification, to come up with a hybrid steganography scheme which can hide more information without having to compromise much on the quality of the stego-image. Experimental results demonstrate that the proposed approach has larger capacity while its results are imperceptible. In comparison with original PVD method criterion of the quality is declined by 2% dB averagely while the …
This paper attempts to improve the quality and the modification rate of a Stego Image. The input image provided for estimating the quality of an image and the modified rate is a bitmap image. The threshold value is used as a parameter for selecting the high frequency pixels from the Cover Image. The data embedding process are performed on the pixels that are found with the help of Threshold value by using LSBMR. The quality of an image is estimated by the value of PSNR and the modification rate of an image is estimated by the value of MSE. The proposed approach achieves about 0.2 to 0.6 % of i…
Seoul National University（首尔大学）；Ewha Womans University（梨花女子大学）；Kangwon National University（江原大学）；Ziovision
Recently, the field of steganography has experienced rapid developments based on deep learning (DL). DL based steganography distributes secret information over all the available bits of the cover image, thereby posing difficulties in using conventional steganalysis methods to detect, extract or remove hidden secret images. However, our proposed framework is the first to effectively disable covert communications and transactions that use DL based steganography. We propose a DL based steganalysis technique that effectively removes secret images by restoring the distribution of the original image…
Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Dublin City University（都柏林城市大学）；Dolby Labs（杜比实验室）；Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学机器人与工业信息研究所）；Barcelona Supercomputing Center（巴塞罗那超级计算中心）
Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual audio steganography setup we propose allows independent encoding of the hidden image from the host audio without compromising quality. According…
Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity…
This paper is to create a practical steganographic implementation to hide color image (stego) inside another color image (cover). The proposed technique uses Five Modulus Method to convert the whole pixels within both the cover and the stego images into multiples of five. Since each pixels inside the stego image is divisible by five then the whole stego image could be divided by five to get new range of pixels 0..51. Basically, the reminder of each number that is not divisible by five is either 1,2,3 or 4 when divided by 5. Subsequently, then a 4-by-4 window size has been implemented to accomm…
National Institute of Technology, Jalandhar（贾朗达尔国家技术学院）
In this paper, A new image steganography scheme is proposed which is a kind of spatial domain technique. In order to hide secret data in cover-image, the first component alteration technique is used. Techniques used so far focuses only on the two or four bits of a pixel in a image (at the most five bits at the edge of an image) which results in less peak to signal noise ratio and high root mean square error. In this technique, 8 bits of blue components of pixels are replaced with secret data bits. Proposed scheme can embed more data than previous schemes and shows better image quality. To prov…
Performance indicators characterizing modern steganographic techniques include capacity (i.e. the quantity of data that can be hidden in the cover medium), stego quality (i.e. artifacts visibility), security (i.e. undetectability), and strength or robustness (intended as the resistance against active attacks aimed to destroy the secret message). Fibonacci based embedding techniques have been researched and proposed in the literature to achieve efficient steganography in terms of capacity with respect to stego quality. In this paper, we investigated an innovative idea that extends Fibonacci-lik…
As communication channels are increasing in number, reliability of faithful communication is reducing. Hacking and tempering of data are two major issues for which security should be provided by channel. This raises the importance of steganography. In this paper, a novel method to encode the message information inside a carrier image has been described. It uses Karhunen-Loève Transform for compression of data and Least Bit Substitution for data encryption. Compression removes redundancy and thus also provides encoding to a level. It is taken further by means of Least Bit Substitution. The algo…
We exploit human color metamers to send light-modulated messages less visible to the human eye, but recoverable by cameras. These messages are a key component to camera-display messaging, such as handheld smartphones capturing information from electronic signage. Each color pixel in the display image is modified by a particular color gradient vector. The challenge is to find the color gradient that maximizes camera response, while minimizing human response. The mismatch in human spectral and camera sensitivity curves creates an opportunity for hidden messaging. Our approach does not require kn…
Currently, cryptography is in wide use as it is being exploited in various domains from data confidentiality to data integrity and message authentication. Basically, cryptography shuffles data so that they become unreadable by unauthorized parties. However, clearly visible encrypted messages, no matter how unbreakable, will arouse suspicions. A better approach would be to hide the very existence of the message using steganography. Fundamentally, steganography conceals secret data into innocent-looking mediums called carriers which can then travel from the sender to the receiver safe and unnoti…
National Institute of Informatics（国立信息学研究所）；Feng Chia University（逢甲大学）；Fujian Polytechnic Normal University（福建技术师范学院）；Tokyo Metropolitan University（东京都立大学）
Artificial neural networks have advanced the frontiers of reversible steganography. The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data. Residual modulation is recognised as the most advanced reversible steganographic algorithm for digital images. The pivot of this algorithm is predictive analytics in which pixel intensities are predicted given some pixel-wise contextual information. This task can be perceived as a low-level vision problem and hence neural networks for addressing a similar class of problems can be deployed. On to…
Retention of secrecy is one of the significant features during communication activity. Steganography is one of the popular methods to achieve secret communication between sender and receiver by hiding message in any form of cover media such as an audio, video, text, images etc. Least significant bit encoding is the simplest encoding method used by many steganography programs to hide secret message in 24bit, 8bit colour images and grayscale images. Steganalysis is a method of detecting secret message hidden in a cover media using steganography. RS steganalysis is one of the most reliable stegan…
Recent advances on adaptive steganography show that the performance of image steganographic communication can be improved by incorporating the non-additive models that capture the dependences among adjacent pixels. In this paper, a Gaussian Markov Random Field model (GMRF) with four-element cross neighborhood is proposed to characterize the interactions among local elements of cover images, and the problem of secure image steganography is formulated as the one of minimization of KL-divergence in terms of a series of low-dimensional clique structures associated with GMRF by taking advantages of…
Digital steganography is becoming a common tool for protecting sensitive communications in various applications such as crime(terrorism) prevention whereby law enforcing personals need to remotely compare facial images captured at the scene of crime with faces databases of known criminals(suspects); exchanging military maps or surveillance video in hostile environment(situations); privacy preserving in the healthcare systems when storing or exchanging patient medical images(records); and prevent bank customers accounts(records) from being accessed illegally by unauthorized users. Existing digi…
Image Steganography is the process of embedding text in images such that its existence cannot be detected by Human Visual System (HVS) and is known only to sender and receiver. This paper presents a novel approach for image steganography using Hue-Saturation-Intensity (HSI) color space based on Least Significant Bit (LSB). The proposed method transforms the image from RGB color space to Hue-Saturation-Intensity (HSI) color space and then embeds secret data inside the Intensity Plane (I-Plane) and transforms it back to RGB color model after embedding. The said technique is evaluated by both sub…
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…
An efficient 2-step steganography technique is proposed to enhance stego image quality and secret message un-detectability. The first step is a preprocessing algorithm that reduces the size of secret images without losing information. This results in improved stego image quality compared to other existing image steganography methods. The proposed secret image size reduction (SISR) algorithm is an efficient spatial domain technique. The second step is an embedding mechanism that relies on Fibonacci representation of pixel intensities to minimize the effect of embedding on the stego image qualit…
Steganography involves hiding a secret message or image inside another cover image. Changes are made in the cover image without affecting visual quality of the image. In contrast to cryptography, Steganography provides complete secrecy of the communication. Security of very sensitive data can be enhanced by combining cryptography and steganography. A new technique that uses the concept of Steganography to obtain the position values from an image is suggested. This paper proposes a new method where no change is made to the cover image, only the pixel position LSB (Least Significant Bit) values …
This paper developed a brightness enhancement technique for video frame pixel intensity improvement. Frames extracted from the six sample video data used in this work were stored in the form of images in a buffer. Noise was added to the extracted image frames to vary the intensity of their pixels so that the pixel values of the noisy images differ from their true values in order to determine the efficiency of the developed technique. Simulation results showed that, the developed technique was efficient with an improved pixel intensity and histogram distribution. The Peak to Signal Noise Ratio …
This article presents the method of steganography detection, which is formed by replacing the least significant bit (LSB). Detection is performed by dividing the image into layers and making an analysis of zero-layer of adjacent bits for every bit. First-layer and second-layer are analyzed too. Hierarchies analysis method is used for making decision if current bit is changed. Weighting coefficients as part of the analytic hierarchy process are formed on the values of bits. Then a matrix of corrupted pixels is generated. Visualization of matrix with corrupted pixels allows to determine size, lo…
