Electronic Music Distribution (EMD) is undergoing two fundamental shifts. The delivery over wired broadband
networks to personal computers is being replaced by delivery over heterogeneous wired and wireless networks,
e.g. 3G and Wi-Fi, to a range of devices such as mobile phones, game consoles and in-car players. Moreover,
restrictive DRM models bound to a limited set of devices are being replaced by flexible standards-based DRM
schemes and increasingly forensic tracking technologies based on watermarking. Success of these EMD services
will partially depend on scalable, low-complexity and bandwidth eficient content protection systems.
In this context, we propose a new partial encryption scheme for Advanced Audio Coding (AAC) compressed
audio which is particularly suitable for emerging EMD applications. The scheme encrypts only the scale-factor
information in the AAC bitstream with an additive one-time-pad. This allows intermediate network nodes to
transcode the bitstream to lower data rates without accessing the decryption keys, by increasing the scale-factor
values and re-quantizing the corresponding spectral coeficients. Furthermore, the decryption key for each user
is customized such that the decryption process imprints the audio with a unique forensic tracking watermark.
This constitutes a secure, low-complexity watermark embedding process at the destination node, i.e. the player.
As opposed to server-side embedding methods, the proposed scheme lowers the computational burden on servers
and allows for network level bandwidth saving measures such as multi-casting and caching.
Collusion attack is a malicious watermark removal attack in which the hacker has access to multiple copies of the
same content with different watermarks and tries to remove the watermark using averaging. In the literature, several
solutions to collusion attacks have been reported. The main stream solutions aim at designing watermark codes
that are inherently resistant to collusion attacks. The other approaches propose signal processing based solutions
that aim at modifying the watermarked signals in such a way that averaging multiple copies of the content leads
to a significant degradation of the content quality. In this paper, we present signal processing based technique
that may be deployed for deterring collusion attacks. We formulate the problem in the context of electronic music
distribution where the content is generally available in the compressed domain. Thus, we first extend the collusion
resistance principles to bit stream signals and secondly present experimental based analysis to estimate a bound on
the maximum number of modified versions of a content that satisfy good perceptibility requirement on one hand
and destructive averaging property on the other hand.
KEYWORDS: Digital watermarking, Video, Modulation, Signal detection, Video compression, Visual process modeling, Visual system, Sensors, Detection and tracking algorithms, Visualization
Successful watermarking algorithms have already been developed for various applications ranging from meta-data tagging to forensic tracking. Nevertheless, it is commendable to develop alternative watermarking techniques that provide a broader basis for meeting emerging services, usage models and security threats. To this end, we propose a new multiplicative watermarking technique for video, which is based on the principles of our successful MASK audio watermark. Audio-MASK has embedded the watermark by modulating the short-time envelope of the audio signal and performed detection using a simple envelope detector followed by a SPOMF (symmetrical phase-only matched filter). Video-MASK takes a similar approach and modulates the image luminance envelope. In addition, it incorporates a simple model to account for the luminance sensitivity of the HVS (human visual system). Preliminary tests show algorithms transparency and robustness to lossy compression.
As a result of advances in audio compression, availability of broadband Internet access at home and the popularity of electronic music distribution systems, today consumers acquire and store ever-increasing number of songs in their local databases. Moreover, consumer-devices with mass random-access storage and sophisticated rendering capabilities make the whole electronic music database available for instant playback. As opposed to traditional music playback where only a limited number of songs are manually selected, there is a strong need for intelligent play-list generation techniques that utilize the whole database while taking the user's interests into account. Moreover, it is desirable to present these songs in a seamlessly streaming manner with smooth transitions. In this paper, we propose a systematic expressive content retrieval system, called AutoDJ, that achieves both objectives. It automatically creates a play-list by sorting songs ac-cording to their low-level features and plays them in a smooth rhythmically consistent way after audio mixing. AutoDJ first builds a profile for each song using features such as tempo, beat and major. Afterwards, it uses a similarity metric to build up a play-list based on a "seed" song. Finally, it introduces smooth transition from one song (profile) to the other by equalizing the tempo and synchronizing the beat phase. We present the system design principles and the signal processing techniques used, as well as a simple AutoDJ demonstrator.
In recent years we have seen many initiatives to provide electronic music delivery (EMD) services. We observe that a key success factor in EMD is the transparency of the distribution service. We could compare it with the traditional music distribution via compact discs. By buying a CD, a user acquires a 'free' control of the content, i.e. he can copy it, he can play it multiple times etc. In the electronic equivalent, the usage and digital rights management rules should be transparent, and preferably comparable to the classical method of distributing contents.
It is the goal of this paper to describe a technology concept that facilitates, from a consumer perspective simple EMD service. Digital watermarking and fingerprinting are the two key technologies involved. The watermarking technology is used to convey the information that uniquely identifies a specific transaction, and the fingerprint technology is adopted for key management and security purposes. In this paper, we discuss how these two technologies are integrated in such a way that watermark security (i.e. the inability to maliciously alter the watermark) and distribution efficiency (i.e. the ability to serve multiple consumers with one distribution PC) are maximized.
Using wavelet networks, it is possible to capture the characteristics of non-linear dynamic systems in a multi- scale modeling strategy. Starting from the coarsest approximation we go step-wise to the finer scales. At each step the error signal or the residue of the system is modeled. This procedure is repeated until the residual drops below some modeling error bound. The modeling is carried out using compactly supported biorthogonal wavelets. By choosing appropriate wavelet basis, it is possible to obtain a near optimal model.
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