Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2 or bpp (bits per pixel) of 23.57 by changing only 0.76 of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.
Since the least significant bits of the image data should look like random data, there are major two schemes in distributing the hiding data. The first kind of methods is to put in the hiding message sequentially after encrypting or compressing to achieve the randomness. The second kind of methods is scattering the hiding data by adopting a mutually acknowledged random seed by which generates the actual hiding sequence @cite_39 .
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Steganography is the practice of concealing a secret message within another, ordinary, message. Commonly, steganography is used to unobtrusively hide a small message within the noisy regions of a larger image. In this study, we attempt to place a full size color image within another image of the same size. Deep neural networks are simultaneously trained to create the hiding and revealing processes and are designed to specifically work as a pair. The system is trained on images drawn randomly from the ImageNet database, and works well on natural images from a wide variety of sources. Beyond demonstrating the successful application of deep learning to hiding images, we carefully examine how the result is achieved and explore extensions. Unlike many popular steganographic methods that encode the secret message within the least significant bits of the carrier image, our approach compresses and distributes the secret image's representation across all of the available bits. This paper discusses the design of a Robust image steganography technique based on LSB (Least Significant Bit) insertion and RSA encryption technique. Steganography is the term used to describe the hiding of data in images to avoid detection by attackers. Steganalysis is the method used by attackers to determine if images have hidden data and to recover that data. The application discussed in this paper ranks images in a users library based on their suitability as cover objects for some data. By matching data to an image, there is less chance of an attacker being able to use steganalysis to recover the data. Before hiding the data in an image the application first encrypts it. The steganography method proposed in this paper and illustrated by the application is superior to that used by current steganography tools. In many situations, such as in biometric applications, there is need to encrypt and “hide” data, while simultaneously permitting restricted computations on them. We present a method to securely determine the 2 distance between two signals if they are close enough. This method relies on a locality sensitive hashing scheme based on a secure modular embedding, computed using quantized random projections, being a generalization of previous work in the area. Secure Modular Hashes (SMH) extracted from the signals preserve information about the distance between the signals, hiding other characteristic from the signals. Theoretical properties state that the described scheme provides a mechanism to threshold how much information to reveal, and is also information theoretically secure above this threshold. Finally, experimental results reveal that distances computed from SMH vectors can effectively replace the actual Euclidean distances with minimal degradation. In many applications, wireless sensor networks need to secure information. Actual researchs found efficient solutions for this kind of network, principally by using cryptography to secure the data transfer. However an encrypted information send by the network can be sufficient to preventan attacker, who eavesdrops the network, that somethingimportant has been detected. To avoid this situation, we propose another way to secure wireless sensor networks by using steganography, specifically by hiding data in the MAC layer of the 802.15.4 protocol. We show that this solution can be an energy-efficient way with a good latency to hide data in a wireless sensor network. The process of discovering relevant patterns holding in a database was first indicated as a threat to database security by O'Leary in. Since then, many different approaches for knowledge hiding have emerged over the years, mainly in the context of association rules and frequent item sets mining. Following many real-world data and application demands, in this paper, we shift the problem of knowledge hiding to contexts where both the data and the extracted knowledge have a sequential structure. We define the problem of hiding sequential patterns and show its NP-hardness. Thus, we devise heuristics and a polynomial sanitization algorithm. Starting from this framework, we specialize it to the more complex case of spatiotemporal patterns extracted from moving objects databases. Finally, we discuss a possible kind of attack to our model, which exploits the knowledge of the underlying road network, and enhance our model to protect from this kind of attack. An exhaustive experiential analysis on real-world data sets shows the effectiveness of our proposal. Abstract It is a hot topic in the design of information hiding algorithm by a combination of compressed technique and interpolation technique in the future. Higher capacity and better image quality are one of the foremost research contents of information hiding. In recent years, an adaptive information hiding has been a hot content of research in the field of information hiding and network security. This paper proposed an adaptive image steganography using absolute moment block truncation coding compression (AMBTC compression) and interpolation technique (ASAI), which can improve the performance of information hiding scheme. The ASAI scheme proposed can offer the benefits of high embedding capacity with low computational complexity and good image quality. The experimental results demonstrate that the proposed ASAI scheme has superior performance than others. Abstract In this paper, we proposes high payload reversible data hiding scheme using weighted matrix. Generate cover image by enlarge the size of original image using interpolation. Partition the original image into (3 × 3) pixel blocks and cover image into (5 × 5) blocks. Perform modular sum of entry-wise multiplication of an (3 × 3) original pixel block with same size shared predefined weighted matrix W . At most twelve multiplication operations are performed to embed 48 bits secret data within an (5 × 5) overlapped pixel block. In each operation, the data embedding position are identified and stored the positional value by modifying three least significant bits of the interleaved pixel of cover image to hide large amount of secret data. For i -th block i = 1, 2, …, we update the weighted matrix W i +1 as W i +1 = ( W i × κ − 1) mod 9, where gcd ( κ , 9) = 1. The original pixels are not effected during data embedding in our scheme which assure reversibility. The proposed scheme provides average embedding payload 2.97 bits per pixel (bpp) with good visual quality measured by peak signal to noise ratio (PSNR) guaranteed to be higher than 37.97 dB. Finally, we compare our scheme with other state-of-the-art methods and obtain reasonably better performance in terms of data embedding capacity. We want to enable a pair of communicating users to exchange secret messages while hiding the fact that secret communication is taking place. We propose a linguistic steganography approach, where each human message is hidden in another human-like message. A hard open question is how to keep the steganographic message small -- existing related tools tend to blow up its size, thereby revealing the use of steganography. We encrypt by compressing each message, mapping it to a plausible sequence of words (using a language model), and letting the human user edit the outcome to produce a human-like message; we decrypt with a Viterbi-like state decoder. Our approach aims in producing text that a human can edit and fix with minimal effort. As a first step, we build a prototype of our system that helps users encrypt English messages (into English messages), and we report on first experiments on Mechanical Turk. Abstract In recent years, image steganography has been one of the emerging research areas. As the field of information technology is advancing, the need of information security is increasing day by day. Steganography is a widely used communication method in today's scenario which involves sending secret information in appropriate carriers. Since it have an interesting property of concealing the message as well as the existence of the message, steganography is on its evolutionary path to unearth new platforms. As the field of steganalysis is growing exponentially, the need of developing strong steganographic algorithms is also growing. Since the use of steganography is spreading across various fields, the goal of increasing the embedding capacity, security and image quality is being major concerns. We propose a new image steganographic method which is based on random selection of pixels for secret data embedding and post processing the stego-image using Hybrid Fuzzy Neural Networks. The pixels where secret data is to be embedded is selected randomly using a pseudo random key. In the selected pixels the last 2 or 3 bits are used for hiding. The resultant degradation in the quality of stego-image is handled by an efficient pixel adjustment process with the use of fuzzy neural networks.. The experimental results reveal that this method can achieve an embedding capacity of 3 bits per byte with excellent stego-image quality and high imperceptibility. We show that if a connected graph with @math nodes has conductance φ then rumour spreading, also known as randomized broadcast, successfully broadcasts a message within O(φ-1 • log n), many rounds with high probability, regardless of the source, by using the PUSH-PULL strategy. The O(••) notation hides a polylog φ-1 factor. This result is almost tight since there exists graph of n nodes, and conductance φ, with diameter Ω(φ-1 • log n). If, in addition, the network satisfies some kind of uniformity condition on the degrees, our analysis implies that both both PUSH and PULL, by themselves, successfully broadcast the message to every node in the same number of rounds. In this paper we study the trade-offs between time series compressibility and partial information hiding and their fundamental implications on how we should introduce uncertainty about individual values by perturbing them. More specifically, if the perturbation does not have the same compressibility properties as the original data, then it can be detected and filtered out, reducing uncertainty. Thus, by making the perturbation "similar" to the original data, we can both preserve the structure of the data better, while simultaneously making breaches harder. However, as data become more compressible, a fraction of the uncertainty can be removed if true values are leaked, revealing how they were perturbed. We formalize these notions, study the above trade-offs on real data and develop practical schemes which strike a good balance and can also be extended for on-the-fly data hiding in a streaming environment. This work considers data structures based on multi-party computation (MPC) primitives: structuring secret (e.g. secret shared and potentially unknown) data such that it can both be queried and updated efficiently. Implementing an oblivious RAM (ORAM) using MPC allows any existing data structure to be realized using MPC primitives, however, by focusing on a specific example -- a priority queue -- it is shown that it is possible to achieve much better results than the generic solutions can provide. Moreover, the techniques differ significantly from existing ORAM constructions. Indeed it has recently been shown that any information theoretically secure ORAM with n memory locations requires at least log n random bits per read write to hide the access pattern. In contrast, the present construction achieves security with a completely deterministic access pattern. We propose a multi-user Symmetric Searchable Encryption (SSE) scheme based on the single-user Oblivious Cross Tags (OXT) protocol (, CRYPTO 2013). The scheme allows any user to perform a search query by interacting with the server and any ( -1 ) ‘helping’ users, and preserves the privacy of database content against the server even assuming leakage of up to ( -1 ) users’ keys to the server (for a threshold parameter ( )), while hiding the query from the ( -1 ) ‘helping users’. To achieve the latter query privacy property, we design a new distributed key-homomorphic pseudorandom function (PRF) that hides the PRF input (search keyword) from the ‘helping’ key share holders. By distributing the utilized keys among the users, the need of constant online presence of the data owner to provide services to the users is eliminated, while providing resilience against user key exposure. In a functional encryption scheme, secret keys are associated with functions and ciphertexts are associated with messages. Given a secret key for a function f, and a ciphertext for a message x, a decryptor learns f(x) and nothing else about x. Inner product encryption is a special case of functional encryption where both secret keys and ciphertext are associated with vectors. The combination of a secret key for a vector ( x ) and a ciphertext for a vector ( y ) reveal ( x , y ) and nothing more about ( y ). An inner product encryption scheme is function-hiding if the keys and ciphertexts reveal no additional information about both ( x ) and ( y ) beyond their inner product. Predicate encryption schemes are encryption schemes in which each ciphertext Ct is associated with a binary attribute vector and keys Kare associated with predicates. A key Kcan decrypt a ciphertext if and only if the attribute vector of the ciphertext satisfies the predicate of the key. Predicate encryption schemes can be used to implement fine-grained access control on encrypted data and to perform search on encrypted data. Hidden vector encryption schemes [Boneh and Waters --- TCC 2007] are encryption schemes in which each ciphertext is associated with a binary vector and each key Kis associated with binary vector with "don't care" entries (denoted with i¾?). Key Kcan decrypt ciphertext if and only if and agree for all ifor which @math . Hidden vector encryption schemes are an important type of predicate encryption schemes as they can be used to construct more sophisticated predicate encryption schemes (supporting for example range and subset queries). We give a construction for hidden-vector encryption from standard complexity assumptions on bilinear groups of prime order. Previous constructions were in bilinear groups of composite orderand thus resulted in less efficient schemes. Our construction is both payload-hiding and attribute-hiding meaning that also the privacy of the attribute vector, besides privacy of the cleartext, is guaranteed. Linguistic steganography is concerned with hiding information in natural language text. One of the major transformations used in linguistic steganography is synonym substitution. However, few existing studies have studied the practical application of this approach. In this article we propose two improvements to the use of synonym substitution for encoding hidden bits of information. First, we use the Google n-gram corpus for checking the applicability of a synonym in context, and we evaluate this method using data from the SemEval lexical substitution task and human annotated data. Second, we address the problem that arises from words with more than one sense, which creates a potential ambiguity in terms of which bits are represented by a particular word. We develop a novel method in which words are the vertices in a graph, synonyms are linked by edges, and the bits assigned to a word are determined by a vertex coding algorithm. This method ensures that each word represents a unique sequence of bits, without cutting out large numbers of synonyms, and thus maintains a reasonable embedding capacity. Predicate encryption is a generalized notion for public key encryption that enables one to encrypt attributes as well as a message. In this paper, we present a new inner-product encryption (IPE) scheme, as a specialized predicate encryption scheme, whose security relies on the well-known Decision Bilinear Diffie-Hellman (BDH) and Decision Linear assumptions. Our IPE scheme uses prime order groups equipped with a bilinear map and works in both symmetric and asymmetric bilinear maps. Our result is the first construction of IPE under the standard assumptions. Prior to our work, all IPE schemes known to date require non-standard assumptions to prove security, and moreover some of them use composite-order groups. To achieve our goal, we introduce a novel technique for attribute-hiding, which may be of independent interest. How to control the access of the huge amount of big data becomes a very challenging issue, especially when big data are stored in the cloud. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising encryption technique that enables end-users to encrypt their data under the access policies defined over some attributes of data consumers and only allows data consumers whose attributes satisfy the access policies to decrypt the data. In CP-ABE, the access policy is attached to the ciphertext in plaintext form, which may also leak some private information about end-users. Existing methods only partially hide the attribute values in the access policies, while the attribute names are still unprotected. In this paper, we propose an efficient and fine-grained big data access control scheme with privacy-preserving policy. Specifically, we hide the whole attribute (rather than only its values) in the access policies. To assist data decryption, we also design a novel attribute bloom filter to evaluate whether an attribute is in the access policy and locate the exact position in the access policy if it is in the access policy. Security analysis and performance evaluation show that our scheme can preserve the privacy from any linear secret-sharing schemes access policy without employing much overhead. Cryptology is the practice of hiding digital information by means of various obfuscatory and steganographic techniques. The application of said techniques facilitates message confidentiality and sender receiver identity authentication, and helps to ensure the integrity and security of computer passwords, ATM card information, digital signatures, DVD and HDDVD content, and electronic commerce. Cryptography is also central to digital rights management (DRM), a group of techniques for technologically controlling the use of copyrighted material that is being widely implemented and deployed at the behest of corporations that own and create revenue from the hundreds of thousands of mini-transactions that take place daily on programs like iTunes. This new edition of our best-selling book on cryptography and information hiding delineates a number of different methods to hide information in all types of digital media files. These methods include encryption, compression, data embedding and watermarking, data mimicry, and scrambling. During the last 5 years, the continued advancement and exponential increase of computer processing power have enhanced the efficacy and scope of electronic espionage and content appropriation. Therefore, this edition has amended and expanded outdated sections in accordance with new dangers, and includes 5 completely new chapters that introduce newer more sophisticated and refined cryptographic algorithms and techniques (such as fingerprinting, synchronization, and quantization) capable of withstanding the evolved forms of attack. Each chapter is divided into sections, first providing an introduction and high-level summary for those who wish to understand the concepts without wading through technical explanations, and then presenting concrete examples and greater detail for those who want to write their own programs. This combination of practicality and theory allows programmers and system designers to not only implement tried and true encryption procedures, but also consider probable future developments in their designs, thus fulfilling the need for preemptive caution that is becoming ever more explicit as the transference of digital media escalates. * Includes 5 completely new chapters that delineate the most current and sophisticated cryptographic algorithms, allowing readers to protect their information against even the most evolved electronic attacks. * Conceptual tutelage in conjunction with detailed mathematical directives allows the reader to not only understand encryption procedures, but also to write programs which anticipate future security developments in their design. * Grants the reader access to online source code which can be used to directly implement proven cryptographic procedures such as data mimicry and reversible grammar generation into their own work. Anomaly based intrusion detection has been held out as the best (perhaps only) hope for detecting previously unknown exploits. We examine two anomaly detectors based on the analysis of sequences of system calls and demonstrate that the general information hiding paradigm applies in this area also. Given even a fairly restrictive definition of normal behavior, we were able to devise versions of several exploits that escape detection. This is done in several ways: by modifying the exploit so that its manifestations match "normal," by making a serious attack have the manifestations of a less serious but similar attack, and by making the attack look like an entirely different attack. We speculate that similar attacks are possible against other anomaly based IDS and that the results have implications for other areas of information hiding.