Due to rapid development in the network and communication field, it has become necessary to protect the unauthorized duplication of a confidential image. In today’s internet era, fortification of digital gratified during communication is a penurious. The Development of multimedia applications makes digital media to bring about conveniences to the people by easy processing of data. At the same time, it enables the illegal attackers to attack the works. For the protection of data, there has been a growing interest in developing effective techniques to discourage the unauthorized duplication of digital data among Cryptography, Watermarking and Steganography. This paper is a comprehensive review of diverse image processing methods and enormous number of interrelated solicitations in various disciplines, including various cryptography, Steganography, watermarking techniques. In this paper, different existing techniques are discussed along with their drawbacks future scope.
The Growth of hypermedia solicitations on digital media brings about amenities to the people by easy dispensation of data. At the same time, it allows the illegal assailants to bout the works. For the Defense of data, attention has been raising in the emerging operative methods to dishearten the illegal repetition of digital data. Cryptography, Steganography and watermarking provides the copyright protection and authentication.
Information hiding means communicating the information by hiding and retrieving from any digital media. The digital media may be a text, an image, an audio, a video or simply a plain text file. Information hiding is a general term encompassing many sub disciplines. However, generally it encompasses three disciplines: cryptography, watermarking and Steganography.
Steganography is the art and science of writing hidden messages in such a way that no one, apart from the sender and the intended recipient, suspects the existence of the message, a form of security through obscurity. Generally, messages will appear to be something else: images, articles, shopping lists, or some other covert extra and classically, the hidden message may be an invisible link between the visible lines of a private letter. The advantage of steganography over cryptography alone is that the messages do not attract attention to themselves.
As an emerging technology, digital watermarking involves the ideas and theories of different subject coverage, such as signal processing, cryptography, probability theory and stochastic theory, network technology, algorithm design, and other techniques. Digital watermarking hides the copyright information into the digital data through certain algorithm. The secret information to be embedded data will be some text, author's serial number, company logo, or images with some special importance. This secret information is embedded to the digital data (images, audio, and video) to ensure the security, data authentication, identification of owner and copyright protection. The watermark is hidden in the digital data either visibly or invisibly. Watermark is embedded either in spatial or frequency domain. Both the domains are different and have their own pros and cons and are used in different scenarios.
Cryptography is the science of information security. The word is derived from the Greek word kryptos, meaning hidden. Cryptography is closely related to the disciplines of cryptology and cryptanalysis. Cryptography includes techniques such as microdots, merging words with images, and other ways to hide information in storage or transit. However, in today's computer-centric world, cryptography is most often associated with scrambling plaintext (ordinary text) into cipher text (a process called encryption), then back again (known as decryption).
Chae, J.J. and Manjunath, B.S (1999) introduced the video data hiding in which the data hiding used still image watermarking techniques that are extended to video by hiding the messages in each frame independently. A data hiding and extraction procedure for high resolution AVI (Audio Video Interleave) video is proposed. Although AVI videos are large in size, it can be transmitted from a source to target over the network after processing the source video by using this data hiding and extraction procedure securely. There are two different procedures, which are used here at the sender’s end and receiver’s end. The procedures used here are the key for. Data Hiding and Extraction [12]. Splitting the video into individual picture frames and performing data hiding in the frames is a tedious and time consuming process.
Zhang J. et al. (2001) developed new video data hiding techniques which are focused on the characteristics generated by video compressing standards. Motion vector based schemes have been proposed for MPEG algorithms. Motion vectors are calculated by the video encoder in order to remove the temporal redundancies between frames. In these methods, the original motion vector is replaced by another locally optimal motion vector to embed data [2]. This technique may produce artifacts and drift errors, which degrade the video quality. Such errors make reusing of the marked video in the compressed domain very ineffective.
Fotopoulos V., and Skodras A.N. (2003) developed a Transform domain watermarking technique for adaptive selection of the watermark’s position and length. Methods such as spread spectrum were used, where the basic idea is to distribute the message over a wide range of frequencies of the host data. Transform domain is generally preferred for hiding the data since, for the same robustness as for the spatial domain, the result is more pleasant to the Human Visual System (HVS). For this purpose, the Discrete Fourier Transform (DFT), the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) domains were usually employed [3]. First, decoding of the bit stream and then re-encoding it to embed the new data is needed. This results in video quality degradation, since the data hiding process has always some negative impact to the PSNR. Moreover, reusing the marked video, by decoding and re-encoding it, cannot always take place in real time.
Kapotas S.K, et al. (2007) introduced a new method for high capacity data hiding in H.264 streams. This method takes advantage of the different block sizes used by the H.264 encoder during the inter prediction stage in order to hide the desirable data. It is a blind data hiding scheme, i.e. the message can be extracted directly from the encoded stream without the need of the original host video. This fragile data hiding approach can be mainly used for content-based authentication and covert communication [4]. Video systems may introduce some amounts of distortion or artifacts in the signal, so the quality measures are an important problem.
Kim et al. (2012) developed a watermarking technique that is simple to implement, yet quite effective for H.264/AVC. This watermarking scheme inserts a watermark bit on the motion vectors for inter-coded macro blocks or on the mode number for intra-coded macro blocks. A scheme to watermark skipped macro blocks, which are vulnerable to forgery, is also used. The new watermarking scheme shows high watermark payload with small image quality and compression of power degradation. The run-time increases for software implementation and the design overhead for hardware implementation, of the proposed watermark, are very low due to the simplicity of the algorithm [5]. Video systems may introduce some amounts of distortion or artifacts in the signal, so the quality measures are an important problem.
Anumol, et al. (2013) have proposed FPGA implementation of invisible image watermarking algorithm for images. The Discrete Wavelet Transform analyzes the signal at different frequency bands with different resolutions by decomposing the signal into an approximation and detailed information. The decomposition of the signal is done into different frequency bands obtained by successive high pass g(n) and low pass h(n) filtering of the time domain. After the DWT decomposition of secret image and original image, the bits from the Secret image are embedded in to the original input image bits. Convert the bits in the form of an image using Simulink block set and get the Watermarked image. In this work, Vertex 6 and Vertex 4 FPGA devices are used for the implementation of watermarking embedding stage. The extraction stage of the watermarked image is separated out using DWT and IDWT. The group of bits are written into a file and converts the bit into an image using Simulink block set. The watermark must be able to be easily and securely embedded and retrieved by the owner. In this work, Vertex 6 and Vertex 4 FPGA devices are used for the implementation of watermarking extraction stage. DWT decomposition and reconstruction makes data loss, which results in poor quality of the recovered image [6].
Arathi and Chandra (2012) have proposed authentication of images through (LWM) technique with the aid of elliptic curve cryptography. In data embedding process, the signed message is embed into the image by using the LSB method. Initially, the given input image is divided into number of blocks and the number of pixels are selected from every block by vertical raster scanning. Embed the message (information) or host signal into an image by the elliptic curve cryptography. Elliptic Curve Cryptography (ECC) is also called as public key cr yptography, where each user or the device participating in the communication usually have a couple of keys, a public key and a private key, and a set of operations related with the keys to do the cryptographic operations. It has poor randomization in key generation and low security [7].
Baisa (2011) has proposed the wavelet based color image watermarking. This technique decomposes, the cover image using a simple Haar wavelet into four non overlapping multi resolution coefficient sets: LL1, HL1, LH1 and HH1. Performs second level DWT on LL1 to give 4 coefficients: LL2, HL2, LH2 and HH2. Arnold transform is applied the to scramble watermarked image. Then PN sequence is generated depending on KEY and the sum of random sequence say SUM. If SUM > T where, T is some predefined threshold value, is found out and then finds two scrambled images applying Arnold transform with KEY1 and KEY2. Take absolute difference of two scrambled images to give the Final scrambled image. If SUM < T, then apply Arnold transform directly to the watermark image with KEY to get the Final scrambled image. Add Final Scrambled image to HL3 coefficients of the cover image. Take IDWT at level 3, level 2 and level 1, which gives the watermarked color image. Wavelet decomposition and reconstruction cause error during recovery of images [8].
Devapriya and Ramar (2010) have proposed a statistical image watermarking in DWT domain. It embeds the watermarks by modifying the log-scaled singular value of selected coefficients of all sub-bands. In a DWT-based scheme, the DWT coefficients are modified with the data that represents the watermark. Let X = {x1, x2… xN} and Y = {y1, y2… yN} be the vectors representing DWT coefficients of cover image and watermarked image in the HH2 region. For embedding, a bit stream is transformed into a sequence. This sequence is used as the watermark. In this case, the watermark W = {w1, w2… wN} which is chosen from a set M, is embedded into X giving Y. W is inserted into the X by using the multiplicative rule, yi= xi(1 +aiwi) where i = 1, 2, … N; a is the embedding strength and xi, wi and yi are the values of the random variable Xi, Wi and Yi whose probability distribution functions (PDFs) are (si), (wi) and (yi) respectively for i = 1, 2,…N. The elements of the watermarks from the set M are independent and uniformly distributed in the interval [-1, 1]. It is not suitable for all statistical modelling and color image [9].
Dinu (2011) has proposed a data embedding for prediction based reversible watermarking. This method split the expanded difference between the current pixel and its prediction context. The embedding is performed at a lower distortion than the simple embedding of the expanded difference into the current pixel. At detection, the same expanded error is recovered and the extraction of the embedded data and the recovery of the original pixels follow. Embedding data into the prediction context may diminish the performances of further prediction. The increase of the prediction error increases well as distortion is introduced by the watermarking. The optimization of the embedding should be tuned in order to surpass the loss caused by increased prediction error. This method is applicable only to the gray scale image, having Low security of hidden data [10].
Jih, et al. (2010) have proposed a watermarking technique based on DWT associated with embedding rule. 1-level DWT in the host image is obtained and the HL and LH sub bands are divided into non-overlapping blocks of size 2*2. The watermarks are then embedded in the blocks located at the even columns of the HL sub band and the blocks located at the odd columns of the LH sub band. A watermark bit is embedded in a block by modifying the four coefficients in the block according to an embedding rule. To embed a watermark bit, w in a block of size 2*2, mean of the four coefficients is first calculated. Let r be an integer such that 3r ≤ me< 3(r +1), then each of the four coefficients is modified by adding a common value so that the mean of the modified coefficients equals 3r if (w = 0 and r is even) or (w = 1 and r is odd), and equals 3(r+1) if (w = 0 and r is odd) or (w = 1 and r is even). This modification causes the mean value becoming the even or odd multiple of 3 closest to the original mean value depending on the embedded watermark bit w = 0 or 1. It is applicable to a particular statistical model and gray scale image [11].
Sabarinathan, and Manoj (2015) have proposed a Shearlet based watermarking. In today’s internet era, fortification of digital gratified during communication is penurious. Watermarking affords the security for digital content. Robustness of such watermarking procedure is quite low. For increasing the robustness, an approach is introduced, which is the Neuro fuzzy based watermarking embedding process. Conventional methods having information loss during recovery, will be easily hacked, has lower embedding capacity, requires more memory and power consumption. The proposed scheme embeds the binary image over the color image, which uses Shearlet and Inverse Shearlet algorithm for preprocessing of an image and Neuro fuzzy algorithm to embed the bits in green plane of an image. Lower memory requirements, speed of encryption are improved by Neuro fuzzy algorithm [1].
Sandeep and Rajiv (2013) have proposed watermarking using MFHWT. In this method, the watermarking algorithm in the DWT domain and WPT using a Modified Haar Wavelet Transform (MFHWT) evolutionary algorithm were implemented to improve the quality of the image. The algorithm is to decompose the original image using DWT and WPT according to the size of the watermark. Modified Fast Haar Transform (MFHT) will be done by just pleasing (w +x + y + z)/4 instead of (x + y)/2 for approximation and (w +x .y -z)/4 instead of (x- y)/2 for the differencing process. 4 nodes are considered at a time. The calculation for (w+ x .y -z)/4 will yield the aspect coefficients in the level of n-2. To obtain specify coefficients, differencing process (x.y)/2 still require to be done. MFHWT algorithm is used which reduce the memory requirements and the amount of inefficient movement of Haar coefficients. Due to wavelet compression, there is some loss of data and complexity in color image watermarking [13].
Umaamaheshvari and Thanushkodi (2012) have proposed a watermarking technique based on visual cryptography, which embeds multiple binary watermarks into digital medical images based on the concept of Visual Cryptography (VC). Multiple watermarks are embedded as shares in the same image. The secret key K is used as a seed to generate wxh random numbers over the interval [1 to rxc]. Let Ri be the ith random number. A binary matrix A of size wxh is created such that the entries in the array are the most significant bits of Ri, the pixel of the cover image I. A binary matrix C of size wxh is created such that the entries in the array are the most significant bits of the Ri, the random number. Now, both the matrices A and C are bitwise Exclusive-ORed to create a binary matrix B of size w x h. Finally, a Master Share M is created by assigning a pair of bits for each element in the binary matrix B. It is applicable only to a medical image and for the Poor quality of watermark image [14].
Yang and Tsai (2010) have proposed histogram-based reversible data hiding by interleaving predictions. In this method, most pixels are predicted by their two neighboring pixels and four neighboring pixels in the column-based approach and chessboard-based approach respectively. The difference value of each pixel between the original image and the steno image remains within +1. In interleaving predictions, the pixels in odd columns will be predicted by pixels in even columns, then the pixels in even columns are predicted by pixels in odd columns. In the embedding process, predictive error values of odd columns are used to generate a histogram to embed the secret data. Then predictive error values of even columns are used. In the extracting and reversing process, predictive error values of even columns are processed first. Then predictive error values of odd columns are processed. It is based on the spatial domain, so it is less robust to potential attacks [15].
Due to rapid development in the network and communication field, it has become necessary to protect the unauthorized duplication of a confidential image; it is done using authentication techniques. The type of technique used should embed a signal into the host image in order to authenticate image and it is also used for hiding secret image along with high security using encryption key.
The objectives of the work is to provide information which can be understood by anyone for whom it is unintended by means of encrypting the color image. To hide messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message. Security of hidden data is enriched by odd and even bit shuffling during data embedding within the color image. In order to provide Integrity, such information will not be altered in the storage or transit between the sender and the intended receiver without the alteration being detected.
Watermarking will be used to identify and protect copyright ownership. Digital content will be embedded with watermarks depicting metadata identifying the copyright owners.
Digital content is watermarked to indicate that the digital content will not be illegally replicated. Devices capable of replication detects such watermarks and prevents unauthorized replication of the content.
In an image authentication application, the intent is to detect modifications to the data. The characteristics of the image, such as its edges, are embedded and compared with the current images for differences. A solution to this problem could be borrowed from cryptography, where digital signature has been studied as a message authentication method.
Multi-bit watermarking is used to annotate an image. For example, patient records and imaging details related to a medical image will be carefully inserted into the image. This would not only reduce storage space but also provides a tight link between the image and its details. Patient privacy is simply controlled by not keeping the sensitive information as clear text in human readable form, and the watermark will be further secured by encryption.
Conventional methods having poor quality of the recovered image, will be easily hacked, possess lower embedding capacity, requires more memory and power consumption and more hardware resource utilization. In future, the suitable technique selected and rectified the all type of existing problems and also it has a good PSNR value with high quality recovered image than the conventional methods. Also in future, the watermarking and its implementation in FPGA can be extended to the concept of video processing.