LSB Matching and LSB Matching Revisited steganography methods are two general and esiest methods to achieve this aim. Being secured. Fulltext – A Review on Detection of LSB Matching Steganography. LSB matching steganalysis techniques detect the existence of secret messages embedded by LSB matching steganorgaphy in digital media. LSB matching revisited. Least significant bit matching revisited steganography (LSBMR) is a significant improvement of the well-known least significant bit matching algorithm.
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Skip to main content. Log In Sign Up. IV Nov — Dec. Video Steganography deals with hiding secret data or information within a video.
LSB matching revisited – Semantic Scholar
LSB Matching Revisited LSBMR algorithm selects the embedding regions according to the size of secret message and the difference between two consecutive pixels in the cover image. For embedding rates is loweronly sharper edge regions are used while keeping the other smoother regions as they are. In the proposed approach, LSB Matching Revisited algorithm is used to embed the secret message into the video.
Hence large amounts of data can be embedded and also preserving higher visual quality of stego images at the same time. Introduction Steganography is hiding private or secret data within a carrier in invisible manner. It derives from the Greek word steganos, meaning covered or secret and graphy writing or drawing .
The medium where the secret data is hidden is called as cover medium which can be an image, video or an audio file. Any stego algorithm removes the redundant bits in the cover media and inserts the secret data into the space. Higher the quality of video or sound, more redundant bits are available for hiding.
By using lossless steganography techniques, messages can be sent and received securely . Traditionally, steganography was based on hiding secret information in image files. But modern work suggests that there has been growing interest among research fraternity in applying steganographic techniques to video files as well , .
The advantage of using video files in hiding information is the added security against the attack of hacker due to the relative complexity of the structure of video compared to image files. Video based steganographic techniques are broadly classified into temporal domain and spatial domain. In frequency domain, images are transformed to frequency components by using FFT,DCT or DWT and then messages are embedded in some or all of the transformed coefficients.
Embedding may be bit level or block level. Moreover, in spatial domain the bits of the message can be inserted in intensity pixels of the video in LSB positions. The advantage of the method is that the amount of data payload that can be embedded is more in LSB techniques. However most of the LSB techniques are prone to attacks as described in  and . This makes research fraternity interested in designing new methods.
The rest of the paper is arranged as follows, section 2 does Literature survey of the recent steganographic techniques.
In section 3 the proposed video steganographic technique has been described. The proposed algorithm is in section 4. Conclusion and future work are presented in Section 6. Literature Survey Several steganographic methods have been proposed in literature and most of which are performed in pixel domain. However major contribution is in the domain of Image steganography. Masud et al  proposed an LSB technique for RGB true color image by enhancing the existing LSB substitution techniques to improve the security level of hidden information.
Other Examples of LSB schemes can be found in , . It sorts the palette to ensure the difference between two adjacent colors is visually indistinguishable. Tseng and Pan  presented a data hiding scheme in 2-color images, it embeds the information in any bit where at least one of the adjacent bits is the same as the original unchanged bit. These techniques are not www. Video steganography of late has also gained significance for researchers. Various techniques of LSB exists, where  proposes that the data is first encrypted using a key and then embedded in the carrier AVI video file in LSB keeping the key of encryption in a separate file called key file.
Whereas in  selected LSB steganography algorithm is proposed. Other steganography techniques in uncompressed raw video are illustrated in ,  and .
Steganography techniques for compressed video stream can be found in ,  and .
Video steganography scheme based on motion vectors and linear block codes has been proposed in . The flow diagram of the encoding and decoding is given in Fig 1and Fig 2. The cover video is then broken down into frames.
Now the proposed LSB Matching Revisited technique is applied to conceal the data in the carrier frames. The size of the message does not matter in video steganography as the message can be embedded in multiple frames. After concealing data in multiple frames of the carrier video, frames are then grouped together to form a stego video, which is now an embedded video.
In the receiver side, the reverse steps are used to decode the secret data. During decoding, the stego video is again broken into frames. The secret message is extracted from the stego video. Reducing distortion between the cover image and the stego image is an important issue for steganography. Most of the steganographic methods usually use randomly selected pixels for data embedding.
These pixels are selected without considering adjacent pixel values. In such cases the probability of embedding in the smooth regions will be high. Generally, the sharper regions have more complicated statistical features and random characteristics than that at the smoother ones. It is expected that detectable and visual artifacts would be left very low in the sharper regions after data embedding.
It makes the detection more difficult. The details of data embedding and data extraction algorithms are as follows. Encoding technique is given in section 3. Algorithm for Encoding Step 1: Dividing Video into Frames The cover video file is decomposed into number of frames in which the secret message will be hidden.
Shared key is used to select the frame for hiding the message. Calculating the key using Diffie Hellman Algorithm The Diffie-Hellman key exchange method allows two parties who have no prior knowledge of each other to jointly establish a shared secret key over a secure communication channel.
Consider, g as the base, n as a very large prime number or generator. B on receiving the message, extracts n and g. Thus the received key is same at both the ends. This session key was used to encrypt the data which can be transmitted successfully. Embedding the text In the data embedding stage, the scheme first initializes some parameters, which are used for subsequent data preprocessing and region selection, and then estimates the capacity of those selected regions.
If the regions are large enough for hiding the given secret message, then data hiding is performed on the selected regions. Finally, it does some post processing to obtain the stego image.
The cover image of certain size is divided into non-overlapping blocks of pixels. For each small block, we rotate it by a random degree in the range of, as determined by a secret key. The resulting image is rearranged as a row vector by raster scanning. Then the vector is divided into non-overlapping embedding units with every two consecutive pixels, these pixels can be used to generate the pseudorandom number which can be either an even or matchig odd number.
Two benefits can be obtained by the random rotation. First, it can prevent the detector from getting the correct embedding units without the rotation key which improves security. Second, both horizontal and vertical edges pixel pairs within the cover image can be used for data hiding. Therefore, for a given secret message, the threshold for region selection can be determined as follows.
For each unit, we perform the data hiding according to the following four cases. The blocks are then rotated by a random number of degrees based on key.
The process is very similar to Step 1 except that the random degrees are opposite. Then we embed the two parameters mqtching, BZ into a preset region which has not been used for data hiding.
There matchin two parameters in the proposed approach. The first one is the block size BZ for block dividing in data preprocessing, another is the threshold t for embedding region selection. Here, an example is shown. Algorithm for Decoding Step 1: To extract data, we first extract the side information, i. Then do exactly the same revusited as Step 1 in data embedding. The stego image is divided into Bz X Bz blocks and the blocks are then rotated by random degrees based on the secret key key1.
The resulting image is rearranged as a row vector V. Finally, the embedding unit is obtained by dividing V into non overlapping blocks with two consecutive pixels. Travel the embedding units whose absolute differences are greater than or equal to the threshold T according to pseudorandom order based on the secret key key2, until all the hidden bits are extracted completely.
Results And Performance Evaluation Any Steganography technique is characterized mainly by two attributes, imperceptibility and capacity. Imperceptibility means the embedded data must be imperceptible to the observer perceptual invisibility and computer analysis statistical invisibility.
Computer Science > Cryptography and Security
The performance of the proposed technique is evaluated using video stream rhinos. The perceptual imperceptibility of the embedded data is indicated by comparing the original image or video to its stego counterpart so that their visual differences, matchig any, can be determined.
The cover file video details are given in Table 1 and results are tabulated in Table 2. Cover Video File details S. So it lacks from security. In our proposed approach, intruder may not be able to identify the presence of the secret message inside the frame.