Fast Fourier Transform-based steganalysis of covert communications over streaming media

Steganalysis seeks to detect the presence of secret data embedded in cover objects, and there is an imminent demand to detect hidden messages in streaming media. This paper shows how a new steganalysis algorithm based on Fast Fourier Transform (FFT) can be used to detect the existence of secret data embedded in streaming media. The proposed algorithm uses machine parameter characteristics and a network sniffer to determine whether the Internet traffic contains streaming channels. The detected streaming data is then transferred from the time domain to the frequency domain through FFT. The distributions of power spectra in the frequency domain between original VoIP streams and stego VoIP streams are compared in turn using t-test, achieving the p-value of 7.5686E-176 which is below the threshold. Results indicate that the proposed FFT-based steganalysis algorithm is effective in detecting the secret data embedded in VoIP streaming media

STEGANALISIS AUDIO BERBASIS DERIVATIVE SPECTRAL PADA DOMAIN FOURIER

Abstraksi Steganografi data digital saat sekarang ini tidak hanya digunakan untuk kepentingan yang bersifat tidak melanggar hukum namun juga sudah digunakan sebagai cara untuk melakukan tindak kejahatan. Untuk ini perlu adanya pengawasan terhadap pertukaran data untuk mengindikasi apakah dalam suatu objek terdapat pesan rahasia yang berbahaya atau tidak. Steganalisis adalah salah satu metode pada komputer forensik yang digunakan untuk mendeteksi apakah sebuah objek berisi pesan tersembunyi atau tidak. Pada penelitian yang dilakukan oleh Min Ru dikembangkan steganalisis audio berbasis pada kumpulan fitur distorsi. Dan ada pula yang berfokus pada mel-cepstrum yang dikembangkan oleh Kraetzer. Pada tugas akhir ini akan dilakukan steganalisis pada media audio dengan menggunakan Fourier Spectrum dan dilakukan pengembangan dengan menambahkan kombinasi feature-set guna mengetahui kombinasi mana yang paling mempengaruhi deteksi dan menerapkan metode framing yang membagi sampel audio menjadi beberapa bagian untuk memeriksa setiap bagian sampel yang menjadi lokasi penyimpanan pesan. Lalu kemudian Support Vector Machine (SVM) digunakan sebagai classifier untuk menentukan indikasi stego audio. Dengan menerapkan metode ini dibangun sistem mampu mendeteksi stego audio dengan akurasi deteksi tertinggi adalah 78% namun AUC yang kurang memuaskan hanya 51%. Kata kunci: Steganografi, steganalisis, fourier spectrum, derivative spectral, SVM

Steganography integration into a low-bit rate speech codec

Low bit-rate speech codecs have been widely used in audio communications like VoIP and mobile communications, so that steganography in low bit-rate audio streams would have broad applications in practice. In this paper, the authors propose a new algorithm for steganography in low bit-rate VoIP audio streams by integrating information hiding into the process of speech encoding. The proposed algorithm performs data embedding while pitch period prediction is conducted during low bit-rate speech encoding, thus maintaining synchronization between information hiding and speech encoding. The steganography algorithm can achieve high quality of speech and prevent detection of steganalysis, but also has great compatibility with a standard low bit-rate speech codec without causing further delay by data embedding and extraction. Testing shows, with the proposed algorithm, the data embedding rate of the secret message can attain 4 bits / frame (133.3 bits / second)

Universal steganography model for low bit-rate speech codec

Low bit-rate speech codec offers so many advantages over other codecs that it has become increasingly popular in audio communications such as mobile and VoIP (Voice over Internet Protocol) communications, and thus researching steganography in low bit-rate speech codec is of important significance. In this study, we proposed a universal VoIP steganography model for low bit-rate speech codec that uses the PESQ deterioration rate and the decoding error to automatically choose a data embedding algorithm for each VoIP bitstream, which enables ones to achieve covert communications using a low bit-rate speech codec efficiently and securely. Since no or little attention has been paid to steganography in iSAC (Internet Speech Audio Codec), it was chosen as the test codec to verify the effectiveness, security, and practicability of the proposed steganography model. The experimental results show that, with the proposed steganography model, it achieved the average PESQ deterioration rate of 4.04% (less than 5%, indicating strong imperceptibility) and a high data hiding capacity up to 12 bits/frame (400 bits/second, three times larger than other methods), and the proposed steganography model could effectively resist the latest steganalysis

A Method to Detect AAC Audio Forgery

Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate

A Method to Detect AAC Audio Forgery

Article originally published in Endorsed Transactions on Security and SafetyAdvanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rateUS National Institute of Justice and from the US National Science Foundatio