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