Abstract
The increasing development of peer-to-peer networks for delivering and sharing multimedia files poses the problem of how to protect these contents from unauthorized manipulations. In the past few years, a large amount of techniques have been proposed to identify whether a multimedia content has been illegally tampered or not. Nevertheless, very few efforts have been devoted to identifying which kind of attack has been carried out, especially due to the large data required for this task. We propose a novel hashing scheme which exploits the paradigms of compressive sensing and distributed source coding to generate a compact hash signature, and apply it to the case of audio content protection. The audio content provider produces a small hash signature by computing a limited number of random projections of a perceptual, time-frequency representation of the original audio stream; the audio hash is given by the syndrome bits of an LDPC code applied to the projections. At the content user side, the hash is decoded using distributed source coding tools. If the tampering is sparsifiable or compressible in some orthonormal basis or redundant dictionary, it is possible to identify the time-frequency position of the attack, with a hash size as small as 200 bits/second; the bit saving obtained by introducing distributed source coding ranges between 20% to 70%.
Highlights
With the increasing diffusion of digital multimedia contents in the last years, the possibility of tampering with multimedia contents—an ability traditionally reserved, in the case of analog signals, to few people due to the prohibitive cost of the professional equipment—has become quite a widespread practice
The rest of the paper is organized as follows: Section 2 provides the necessary background information about compressive sensing and distributed source coding; Section 3 describes the tampering model; Section 4 gives a detailed description of the system; Section 6 describes how it is possible to estimate the rate of the hash at the encoder without feedback channel or training; the tampering identification algorithm is tested against various kinds of attacks in Section 7, where the different bit-rate requirements for the hash with or without distributed source coding are compared; Section 8 draws some concluding remarks
We compute the SNRP from the projections in place of the whole time-frequency perceptual map of both the signal and the tampering. This is justified by the energy conservation principle stated in (11) and by the fact that, at the content user (CU) side, no information about the authentic audio content is available; this is an approximation of the actual SNRP, which uses the quantized projections obtained by decoding the hash signature, in the reasonable hypothesis that y ≈ y and b ≈ b
Summary
With the increasing diffusion of digital multimedia contents in the last years, the possibility of tampering with multimedia contents—an ability traditionally reserved, in the case of analog signals, to few people due to the prohibitive cost of the professional equipment—has become quite a widespread practice. It is possible to identify tampered regions of the image, at the cost of additional hash bits This scheme has been applied to the case of audio files [17]; instead of random projections of pixels, the authors compute for each signal frame a weighted spectral flatness measure, with randomly chosen weights, and encode this information to obtain the hash. Though this scheme applies well to the authentication task (which can be attained with a hash overhead less than 100 bits/second), it is not clear how to extend the application to identification of general kinds of tampering. The rest of the paper is organized as follows: Section 2 provides the necessary background information about compressive sensing and distributed source coding; Section 3 describes the tampering model; Section 4 gives a detailed description of the system; Section 6 describes how it is possible to estimate the rate of the hash at the encoder without feedback channel or training; the tampering identification algorithm is tested against various kinds of attacks in Section 7, where the different bit-rate requirements for the hash with or without distributed source coding are compared; Section 8 draws some concluding remarks
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