MP3 audio steganography technique using extended least significant bit

Audio Steganography is the process of concealing secret messages into audio file. The goal for using audio steganography is to avoid drawing suspicion to the transmission of the secret message. Prior research studies have indicated that the main properties in steganography technique are imperceptibility, robustness and capacity. MP3 file is a popular audio media, which provides different compression rate and performing steganography in MP3 format after compression is the most desirable one. To date, there is not much research work that embeds messages after compression. An audio steganographic technique that utilizes Standard Least Significant Bits (SLSB) of the audio stream to embed secret message has gained popularity over the years. Unfortunately the technique suffers from imperceptibility, security and capacity. This research offers an extended Least Significant Bit (XLSB) technique in order to circumvent the weakness. The secret message is scrambled before embedding. Scrambling technique is introduced in two steps; partitioning the secret message (speech) into blocks followed by block permutation, in order to confuse the contents of the secret message. To enhance difficulty for attackers to retrieve the secret message, the message is not embedded in every byte of the audio file. Instead the first position of embedding bit is chosen randomly and the rest of the bits are embedded only in even value of bytes of the audio file. For extracting the secret message, the permutation code book is used to reorder the message blocks into its original form. Md5sum and SHA-256 are used to verify whether the secret message is altered or not during transmission. Experimental results measured by peak signal to noise ratio, bit error rate, Pearson Correlation and chi-square show that the XLSB performs better than SLSB. Moreover, XLSB can embed a maximum of 750KB into MP3 file with 30db average result. This research contributes to the information security community by providing more secure steganography technique which provides message confidentiality and integrity

The use of wavelet watermarking and statistical classification techniques for collusion detection and identification in multimedia forensics

This research proposes a wavelet-based multimedia fingerprint scheme and statistical clustering algorithm for collusion detection and identification. The use of digital multimedia has steadily increased using mediums like the Internet. Encryption is generally used to safeguard content while in transmission, but offers no protection against duplication. Tracing unauthorized content distributors has become an increasing concern for the media industry. Unauthorized duplication, piracy, and illegal redistribution of multimedia content account for several billion dollars in losses every year. It is important to design reliable investigative techniques against unauthorized duplication and propagation, and provide protection in the form of theft deterrence. Some fingerprint embedding schemes are robust against single-user modification attacks. However, a new breed of attacks, known as collusion attacks, have been used to defeat those underlying schemes. These attacks use the combination of multiple fingerprinted copies to create a new version where the underlying fingerprint is highly attenuated, becoming untraceable to the colluders. This research adopts the use of wavelet transforms and statistical classification techniques to effectively identify the set of colluders involved in a collusion attack while maintaining low miss rates and false accusation rates. The experimental results show that the solution is effective in identifying large colluder sets without the knowledge of the number of colluders involved in an attack and the collusion attack used