Abstract — We present a watermarking procedure to embed copyright protection into digital video. Our watermarking procedure is scene-based and video dependent. It directly exploits spatial masking, frequency masking, and temporal properties to embed an invisible and robust watermark. The watermark consists of static and dynamic temporal components that are generated from a temporal wavelet transform of the video scenes. The resulting wavelet coefficient frames are modified by a perceptually shaped pseudorandom sequence representing the author. The noise-like watermark is statistically undetectable to thwart unauthorized removal. Furthermore, the author representation resolves the deadlock problem. The multiresolution watermark may be detected on single frames without knowledge of the location of the frames in the video scene. We demonstrate the robustness of the watermarking procedure to several video degradations and distortions. Index Terms — Copyright protection, data hiding, perceptual masking, video watermarking.

Abstract We introduce the notion of associative one-way functions and prove that they exist if and only if P 6 = NP. As evidence of their utility, we present two novel protocols that apply strong forms of these functions to achieve secret key agreement and digital signatures.

We explain how to apply statistical techniques to solve several language-recognition problems that arise in cryptanalysis and other domains. Language recognition is important in cryptanalysis because, among other applications, an exhaustive key search of any cryptosystem from ciphertext alone requires a test that recognizes valid plaintext. Written for cryptanalysts, this guide should also be helpful to others as an introduction to statistical inference on Markov chains. Modeling language as a finite stationary Markov process, we adapt a statistical model of pattern recognition to language recognition. Within this framework we consider four welldefined language-recognition problems: 1) recognizing a known language, 2) distinguishing a known language from uniform noise, 3) distinguishing unknown 0th-order noise from unknown 1st-order language, and 4) detecting non-uniform unknown language. For the second problem we give a most powerful test based on the Neyman-Pearson Lemma. For the oth...

Abstract We propose associative one-way functions as a new cryptographic paradigm for exchanging secret keys and for signing digital documents. First, we precisely define these functions and establish some of their basic properties. Next, generalizing a theorem of Selman, we constructively prove that they exist if and only if P 6 = NP. In addition, we exhibit an implementation based on integer multiplication. We present a novel protocol that enables two parties to agree on a secret key, and we discuss the security of this protocol. Finally, we generalize our protocol to enable two or more parties to agree on a secret key, and we present a similar protocol for signing documents.

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In this thesis, we introduce a new technique to automate the verification of mobile code. Using dataflow analysis techniques, the verifier can check whether data passed between trusted software components is illegally modified by untrusted mobile code. We show that this analysis is powerful enough to make significant guarantees about whether the program will access system resources safely. Furthermore, by rendering the verification transparent to the user, the security system is not vulnerable to human error, or dependent on the user's technical abilities. Other verification techniques do not share these advantages. We describe what requirements enable this analysis, explore its limitations, and present prototype software that implements the idea.

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