F5 Algorithm This algorithm use subtraction or matrix format technique to

# F5 algorithm this algorithm use subtraction or matrix

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will describe two detection algorithms F5 and OutGuess [24]. F5 Algorithm: This algorithm use subtraction or matrix format technique to predict the length of the embedded secret message. This algorithm is the most accurate one to find the length. The central concept that the investigator can do in this algorithm is to replace the least significant bit (LSB) of the DCT coefficient by using the following algorithm [24]: Figure 8 F5 algorithm [24]
OutGuess Algorithm: The outGuess algorithm is designed by Provos to counter the statistical Chi-square attack. It shows that the investigator can detect the stego image by using a pseudo-random number generator. Also, it depends on replacing the least significant bit (LSB) of the DCT coefficient. OutGuess selects the histogram of the DCT coefficient randomly to match the cover and stego histogram. Following the next algorithm will allow the investigator to detect the stego image [24]. Detection Techniques for Audio Steganography Audio forensics analysis is a complex science. The implementation of audio forensic has led to a successful case investigation. Available audio tampering on markets makes the authenticity of audio file detection vital, which in turn results in the critical role of audio forensics crime investigating and exposure. Detecting mechanism of the hidden information existing in audio files refers to Steganalysis. The Electronic Network Frequency (ENF) is one of the recordings of forensic analysis methods. It relies on the traces of the ENF existing in the record [25]. Based on the way phase coding method works by substituting the phase of a first audio segment with a reference data phase to be hidden, which adjudicates the alteration of phase difference because of the extrinsic continuities corruption of unwrapped phase in each section. Therefore, each segment has a different statistical analysis and can be used in monitoring the change, classify the embedded signal, and clean signal. De facto Phase steganalysis is one of the most challenging in computer forensics fields. However, investigators can implement phase steganalysis by dividing each audio signal into segments Figure 9 OutGuess algorithm [24]
with a given length and then perform the following steps. First, they use Fast Fourier Transform (FFT) that allows viewing the spectrum content of an audio signal of a particular segment to drive the phase differential spectra from unwrapped phases of each audio sample. Second, five statistical characteristics of the phase difference for steganalysis are derived. These characteristics are essential because they compress each spectrum information and monitor the change of phase difference: variance, skewness, kurtosis, median, and mean absolute deviation. Finally, they can utilize the support vector machine SVM classifier for classification [26].

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