The evaluation of digital watermarks has grown into an important research area. The usage of profiles for application
oriented evaluation provides an efficient and aimed strategy to evaluate and compare watermarking schemes according to
a predefined watermark parameter setting. Based on first evaluations in [5, 16], in this paper, we present a new
application profile (called Biometrics) and exemplary test results for audio watermarking evaluations. Therefore, we
combine digital watermark evaluation and a biometric authentication system. This application scenario is as follows:
Audio watermarking schemes are used to embed metadata into the speech reference data of the biometric speaker
recognition system. Metadata in our context may consist of feature template representations complementary to the speech
modality, such as iris codes or biometric hashes, ancillary information about the social, cultural or biological context of
the owner of the biometric data or technical details of the sensor. As during watermark embedding the cover signal is
changed, the test results show how different watermarking schemes affect the biometric error rates and speech
performance of the biometric system depending on the embedding capacity and embedding transparency. Therefore, the
biometric error rates are measured for each watermarking scheme, the effect of embedding is shown, and its influence is
analyzed. The transparency of an embedding function is manly measured with subjective or objective tests. In this paper,
we introduce a new objective test called Biometric Difference Grade (BDG) to evaluate the quality for biometric speech
signal modifications on the example of four audio watermarking algorithms.
The evaluation of transparency plays an important role in the context of watermarking and steganography algorithms. This paper introduces a general definition of the term transparency in the context of steganography, digital watermarking and attack based evaluation of digital watermarking algorithms. For this purpose the term transparency is first considered individually for each of the three application fields (steganography, digital watermarking and watermarking algorithm evaluation). From the three results a general definition for the overall context is derived in a second step. The relevance and applicability of the definition given is evaluated in practise using existing audio watermarking and steganography algorithms (which work in time, frequency and wavelet domain) as well as an attack based evaluation suite for audio watermarking benchmarking - StirMark for Audio (SMBA). For this purpose selected attacks from the SMBA suite are modified by adding transparency enhancing measures using a psychoacoustic model. The transparency and robustness of the evaluated audio watermarking algorithms by using the original and modifid attacks are compared. The results of this paper show hat transparency benchmarking will lead to new information regarding the algorithms under observation and their usage. This information can result in concrete recommendations for modification, like the ones resulting from the tests performed here.
Proc. SPIE. 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII
KEYWORDS: Transparency, Image compression, Detection and tracking algorithms, Wavelets, Digital watermarking, Time metrology, Signal processing, Algorithm development, Signal detection, Information security
The evaluation of digital watermarks is an active and important research area. From the variety there are different types of attacks like geometric attacks, lossy compression, security or protocol attacks [1, 2] available to evaluate the robustness of digital watermarks. Furthermore, different attack strategies like single attacks or profile attacks are known to improve the evaluation process . If for example, the robustness of a watermarking algorithm is evaluated, then the signal of the audio content and the embedded watermark is modified with the goal to remove or weaken the watermark information. In this paper, the focus is set on audio signals. We introduce the evaluation process of an existing benchmark service, the Audio Watermark Evaluation Testbed (Audio WET)  by evaluating five different audio watermarking algorithms, which work in time, frequency and wavelet domain. Therefore, we introduce basic, extended and application profiles which improve the evaluation of watermarking algorithms to provide comparability. Whereas the basic profiles measure single properties on a watermarking algorithm, the extended and application profiles reflect real world application scenarios. Furthermore a test scenario and test environment for the evaluation of five audio watermarking algorithms by using basic profiles is described and discussed. The test results of the first evaluation by using basic profiles are introduced and a comparison of the evaluated watermarking algorithms using different parameter sets for embedding function is provided.
Methodologies and tools for watermark evaluation and benchmarking facilitate the development of improved watermarking techniques. In this paper, we want to introduce and discuss the integration of audio watermark evaluation methods into the well-known web service Watermark Evaluation Testbed (WET). WET is enhanced by using. A special set of audio files with characterized content and a collection of single attacks as well as attack profiles will help to select special audio files and attacks with their attack parameters.
Proc. SPIE. 5306, Security, Steganography, and Watermarking of Multimedia Contents VI
KEYWORDS: Signal to noise ratio, Transparency, Image compression, Detection and tracking algorithms, Interference (communication), Distortion, Digital watermarking, Signal processing, Information security, Computer security
Digital watermarking is envisaged as a potential technology for copyright protection and manipulation recognition. A key issue in the usage of robust watermarking is the evaluation of robustness and security. StirMark Benchmarking has been taken to set a benchmarking suite for audio watermarking in addition to existing still image evaluation solutions. In particular we give an overview of recent advancements and actual questions in robustness and transparency evaluations, in complexity and performance issues, in security and capacity questions. Further more we introduce benchmarking for content-fragile watermarking by summarizing design aspects and concluding essential benchmarking requirements.
MPEG-4 is an international object-based standard that provides technological basis for digital television, interactive graphics and multimedia applications. These objects can be natural or synthetic e.g. textures, 3D objects, videos or sounds. In this paper we suggest an integrity approach to protect the content of MPEG-4 data. The essential part of this approach is to embed a robust watermark into each visual, audio and 3D object. The content fragile watermark verifying the integrity of a scene is the sum of all information retrieved from the robust watermarks extracted from the objects of the scene. The information of the fragile watermark will be distributed redundantly to all robust watermarks of the scene. Another essential part of our approach is to embed a part of the scene description or object descriptors as a watermark message into the video or audio streams. The amount of embedded information depends on the payload of the watermarking algorithms. We also analyze the possibility of embedding equivalent information into 3D models, depending on the application.
StirMark Benchmark is a well-known evaluation tool for watermarking robustness. Additional attacks are added to it continuously. To enable application based evaluation, in our paper we address attacks against audio watermarks based on lossy audio compression algorithms to be included in the test environment. We discuss the effect of different lossy compression algorithms like MPEG-2 audio Layer 3, Ogg or VQF on a selection of audio test data. Our focus is on changes regarding the basic characteristics of the audio data like spectrum or average power and on removal of embedded watermarks. Furthermore we compare results of different watermarking algorithms and show that lossy compression is still a challenge for most of them. There are two strategies for adding evaluation of robustness against lossy compression to StirMark Benchmark: (a) use of existing free compression algorithms (b) implementation of a generic lossy compression simulation. We discuss how such a model can be implemented based on the results of our tests. This method is less complex, as no real psycho acoustic model has to be applied. Our model can be used for audio watermarking evaluation of numerous application fields. As an example, we describe its importance for e-commerce applications with watermarking security.