Transient detection-based adaptive audio watermarking using attack-aware optimization

There has been a continued need for technology to prevent the illegal use of music in online transactions and protect the rights of copyright holders. Recently, the types of attacks have become very diverse due to various online environments and content production methods. Therefore, an adaptive protection method aware of content and attacks is needed. This paper proposes a robust watermarking algorithm of a blind method to protect audio copyright in online streaming services. After detecting the transient points of the audio and processing the signals, they are used as reference triggers for frequency conversion. Transient triggers should be determined to be detected even after an attack. Audio samples are converted into the fast Fourier transform (FFT) frequency coefficients, and a watermark is embedded using the quantization index modulation (QIM) method. Various user-defined parameters can be adjusted during the watermark embedding process. Such parameters may be determined through experiments or an optimization process by targeting both the quality of the watermarked audio and the bit error rate (BER) of the watermark extracted after the attack. A watermark can be extracted using a similar process using parameters used in watermark embedding. If there is no information about the parameters used in embedding, the watermark can be extracted by tracking the minimum BER of the extracted watermark. Compared to the existing methods, the proposed watermarking technique showed excellent performance against most attacks and superior performance for filtering, shifting, and moving picture expert group (MPEG)-2 audio layer 3 (MP3) compression attacks. Therefore, the proposed method is likely a good solution for protecting audio copyright in an online streaming environment.