If only one echo is produced from the original signal, then only one bit of information could be encoded. It can be done by substituting the phase of an initial audio segment with a reference phase that represents the data.
It encodes the message bits as phase shifts in the phase spectrum of a digital signal. If the parity bit of the selected region does not match the secret bit to be encoded. It is used to encode a category of information by spreading the encoded data across the frequency spectrum.
Tone insertion method can resist attacks such as low-pass filtering and bit truncation addition to low embedding capacity 4. Video Steganography: The main aim of video steganography is to hide the data from others and main secrecy of which is going to be transmitted. The messages do not know the third person.
The message is unknown to others except for sender and receiver. Due to these weaknesses, a human cannot observe very minor changes in the vision. So, the data can be hidden from other people with the help of videos. A video contains various frames which can be played back the video at fixed frame rates. The video size can also be compressed by some techniques. Conclusion: Steganography is a technique which is used to hide the information but there is no guarantee of hiding the complete information without proper security.
The data can be stored in different formats like image, video, audio, etc. In recent years, hiding the data with confidentiality is more important so many of the people are showing interest towards the Steganography. References: 1. Arvind Kumar and Km. Published By Foundation of Computer Science. Jayati Bhadra, A. Bojamma, Prasad. Falesh M. Shelke, Miss. Ashwini A. Dongre, Mr. Pravin D. Kamred Udham Singh Int. Related Papers. Exploring steganography: Seeing the unseen.
By Sushil Jajodia. Trends in steganography. By Hazeem Taher. Steganography via internet. By Mirza Mudassar. Download pdf. Publication Type. More Filters. The emerging growth in use of digital data recommended the need of effective measure to ensure security of digital data. The goal of steganography is to hide information by concealing it into some … Expand.
View 2 excerpts, cites methods. Image Steganography is a technique of providing some hidden data into the cover or host image so that it can be transmitted in a secure manner. There are various Image Steganography techniques … Expand. View 1 excerpt, cites background. Survey on Techniques for Steganography of Audio Files. The rise and development of multimedia and digital information has made communication easier and quicker.
However with digital information, comes a responsibility to communicate securely and … Expand. Security in data communication is a very important concern today. It is used in almost every region like ecommerce, education, and industry and data warehouse. Steganography literally means secret … Expand. Steganography: Cause and Effect. Novel Steganography Technique for Information Hiding.
Steganography is the art of hiding information in ways that prevent the detection of hidden messages. The probability of embedding is determined by calcu- DCT coefficients at random. The least-significant bit of a lating p for a sample from the DCT coefficients. The sam- selected DCT coefficient is replaced with encrypted ples start at the beginning of the image; for each measure- message data see Figure 6. Instead of increasing OutGuess 0. Its detection rate depends on Figure 6.
The OutGuess 0. We characterize their respective rela- discrete cosine transform DCT coefficients with message data. With a false-positive rate of less than 0. Due to the heuristic, the detection rate for embedded content Detection rate with a change rate of 5 percent is greater than 40 percent 0. One of us Niels Provos showed that applying cor- 0. If the statistical tests used to examine an image for steganographic content are known, it is possible 0 to use the remaining redundant bits to correct statistical 0 0.
In this case, preserving the DCT frequency histogram prevents ste- Figure 7. The image and non-stego image. The change rate refers to the fraction of discrete cosine inates between the two classes. The discrimination func- transform DCT coefficients available for embedding a hidden tion determines the class of a new image that is not part of message that have been modified. The set of statistics used by the discrimi- nation function is called the feature vector.
Lyu and his colleague used a support vector machine Using the extended test, we can detect pseudo-randomly SVM to create a nonlinear discrimination function. Detection rate PD for a nonlinear support vector machine.
Using four differ- and the hypothesis H1 that the new image contains a hid- ent scales, a program or a researcher calculates a den message. For the binary hypothesis problem, detection theory Table 1 shows their achieved detection rate using a non- provides us with the Neyman-Pearson criterion, which linear SVM for false-positive rates 0. For different types of images—for example, nature px H 0 X H 0 H 0 scenes and indoor photographs—the detection rate could decrease when using a single training set.
The note the joint probability functions for x1, x2, …, xk probability models for clutter in natural images that Ulf under H1 and H0, respectively. To choose the weights bi, we assume that the set xi of We can improve the detection quality rate by using a non-stego images and the set yi of stego images are inde- feature vector based on different statistics.
Instead of using pendently and normally distributed. This assumption lets a wavelet-like decomposition, we look at the distribution us calculate the probability functions px H1 X H1 and of squared differences, px H0 X H0 , which we use to derive the weights bi. The second set of statistics is based on the errors in an op- where i enumerates the number of blocks in the image, timal linear predictor of coefficient magnitude.
For each and k enumerates the rows or columns in a single block. Different feature vectors based on wavelet-like decomposition and on squared differences.
Figure 8 compares the linear discrimination functions Experimental evidence shows that the blockiness B derived from the two feature vectors. Figure 8a shows re- increases monotonically with the number of flipped ceiver-operating characteristics ROC for OutGuess least-sequential bits in the DCT coefficients.
For OutGuess, the feature vectors show com- for the cover image and decreases for an image that al- parable detection performance. However, for F5, the ready contains a message.
Jessica Fridrich and 1. Determine the blockiness BS 0 of the decompressed her colleagues made a steganalytic attack on OutGuess stego image.
Crop the stego image by four pixels to reconstruct an method independent of the DCT histogram. They used image similar to the cover image. The discontinues are measured by the 4. Using OutGuess, embed a maximal length message blockiness formula into the cropped image and calculate the resulting blockiness B 1. The repeat average of the p-values is taken as the final message length.
This approach has two advan- decrement absolute value of DCT coefficient Gs tages over class discrimination: it does not require a training insert Gs into stego image set and it determines the length of hidden messages. The F5 algorithm. F5 uses subtraction and matrix encoding tem, F5. Instead of replacing the least-significant bit of a DCT coefficient with message data, F5 decrements its absolute value in a process called matrix encoding.
If the coefficient becomes zero, shrinkage happens, from the message length and the number of nonzero non- and it is discarded from the coefficient group. The group is DC coefficients. The Hamming code 1, 2k— 1, k encodes filled with the next nonzero coefficient and the process re- a k-bit message word m into an n-bit code word a with peats until the message can be embedded. It can recover from a single bit error in the code For smaller messages, matrix encoding lets F5 reduce word.
With matrix encoding, embedding total code size more than doubles. Because F5 decre- any k-bit message into any n-bit code word changing it at ments DCT coefficients, the sum of adjacent coefficients most by one bit.
Given a code However, Fridrich and her group presented a stegan- word a and message word m, we calculate the difference s alytic method that does detect images with F5 content. We do this by decompressing the stego image into the spatial 0. The resulting image is then cropped by four pix- els on each side to move the errors at the block bound- 0. We recompress the cropped image using the same quantization tables as the stego image, getting the esti- 0.
Fridrich and her group proposed a method for 0. To get a better under- 0. In the first test, both types 0 0 0. The only False-positive rate difference is that the stego images contain a stegano- graphic message. Notice that the false-positive rate is Figure Receiver-operating characteristics ROCs of the F5 detection fairly high compared to the detection rate. The second algorithm.
The detection rate is analyzed when using double test uses the original JPEG images without double com- compression elimination and against single compressed images. Statistics-aware embedding d.
Huv d is the corresponding function for the stego So far, we have presented embedding algorithms that image. As work for an embedding algorithm that uses global image F5 changes coefficients pseudo randomly, we expect the statistics to influence how coefficients should be changed. They found the best correspondence when the change to image statistics.
This leads to and the coefficient histogram. OutGuess, for exam- 0.
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