# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 3 Proposed Method  
IWT along with the JPEG compression video steganography technique is the one that balances concealing capacity, imperceptibility, and robustness. IWT needs low power and is much more effective in terms of memory. IWT technique works on integer coefficients. With integer coefficients, calculations take less time, and chances of error are also reduced. Hardware execution of IWT is also simple due to its integer nature. To authenticate the  
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scattering output calculates covariance (\(\sigma_{Xn}x_{m}\)) of sub-bands, by considering two at a time. Covariance can be expressed as in Equation (1) [20]:  
\[\sigma_{X_{n}X_{m}}=(X_{n}X_{m})^{c}-(X_{n})^{c}(X_{m})^{c} \tag{1}\]  
As in Equation (1), the value of variance is found to be lowest for HH and LL sub-bands. Figure 3 shows the decomposition of input signal or video into LL and HH components [21]. Low Pass Filter (LVF) converts the input signal into LL coefficients and High Pass Filter (HPF) converts input into HH coefficients. LL coefficients are the low-frequency content and are known as an approximation. HH coefficients are high-frequency content and are known as details. LL coefficients carry the finest detail of image and HH coefficients carry noise. The process of decomposing the signal is known as signal analysis. During signal analysis twice coefficients are produced so to reduce the number of coefficients decimation of coefficients is done by down sampling filter output by 2. Reduced coefficients are represented as LL * and HH *. The whole process of decimation is shown in Figure 4 as follows [19]. During decomposition only LL coefficients are considered. As LL coefficients have important information and HH coefficients have noise. Every time during decomposition, noise is removed from the information part and lastly the refined information is retrieved. The process of decomposition of coefficients into lower resolution coefficients goes up to log2N levels. Where N is the length of the input signal. Every time LL coefficients are decomposed into LL and HH coefficients. LL coefficients give refined detail of signal and noise is eliminated from the signal in the form of HH coefficients every time due to decomposition. The decomposition tree is shown in Figure 5. In the given figure, three level decomposition of the signal is done. It can be decomposed further but up to the three level most of the noise is eliminated. So, researcher and academician generally do three levels of decomposition of the given input file or signal. Figure 3: Approximation and Detail Coefficients. After a complete analysis of the input signal by using inverse IWT signals, synthesis takes place. In signal synthesis, approximation coefficients are upsampled by 2 in each step up to log2N levels. Upsampling of the signal is explained with the help of Figure 6 as follows. In signal synthesis the reduced coefficients are added back to the signal to regenerate the original signal. This process takes place at the receiver side when receiver extract the hidden secret message. Figure 4: Signal Analysis. Figure 5: Wavelet Decomposition. By signal synthesis, an up to log2N original signal is generated. Figure 7 shows the flow of signal reconstruction by using inverse IWT. In the complete process, almost no loss of information occurs because of the integer nature of the IWT technique. IWT reconstruct the original signal from the integer coefficients. It provides lossless reconstruction of signal due to its integer nature. Because of its simple implementation it requires less hardware resulting into low cost. The third technique is IWT, which helps to reduce the computational overhead. There are many conventional data hiding techniques such as DWT, DCT, LSB, MSB, and many more. However, among all these techniques IWT is chosen because of its intrinsic properties. Some of the features of IWT are listed as follows [22]:  
Figure 6: Signal Synthesis. Figure 7: Signal Reconstruction.