Semifragile Speech Watermarking Based on Least Significant Bit Replacement of Line Spectral Frequencies

There are various techniques for speech watermarking based on modifying the linear prediction coefficients (LPCs); however, the estimated and modified LPCs vary from each other even without attacks. Because line spectral frequency (LSF) has less sensitivity to watermarking than LPC, watermark bits are embedded into the maximum number of LSFs by applying the least significant bit replacement (LSBR) method. To reduce the differences between estimated and modified LPCs, a checking loop is added to minimize the watermark extraction error. Experimental results show that the proposed semifragile speech watermarking method can provide high imperceptibility and that any manipulation of the watermark signal destroys the watermark bits since manipulation changes it to a random stream of bits

Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p

MeshAdv: Adversarial Meshes for Visual Recognition

Highly expressive models such as deep neural networks (DNNs) have been widely applied to various applications. However, recent studies show that DNNs are vulnerable to adversarial examples, which are carefully crafted inputs aiming to mislead the predictions. Currently, the majority of these studies have focused on perturbation added to image pixels, while such manipulation is not physically realistic. Some works have tried to overcome this limitation by attaching printable 2D patches or painting patterns onto surfaces, but can be potentially defended because 3D shape features are intact. In this paper, we propose meshAdv to generate "adversarial 3D meshes" from objects that have rich shape features but minimal textural variation. To manipulate the shape or texture of the objects, we make use of a differentiable renderer to compute accurate shading on the shape and propagate the gradient. Extensive experiments show that the generated 3D meshes are effective in attacking both classifiers and object detectors. We evaluate the attack under different viewpoints. In addition, we design a pipeline to perform black-box attack on a photorealistic renderer with unknown rendering parameters.Comment: Published in IEEE CVPR201

Optimization of a Blind Speech Watermarking Technique against Amplitude Scaling

This paper presents a gain invariant speech watermarking technique based on quantization of the Lp-norm. In this scheme, first, the original speech signal is divided into different frames. Second, each frame is divided into two vectors based on odd and even indices. Third, quantization index modulation (QIM) is used to embed the watermark bits into the ratio of the Lp-norm between the odd and even indices. Finally, the Lagrange optimization technique is applied to minimize the embedding distortion. By applying a statistical analytical approach, the embedding distortion and error probability are estimated. Experimental results not only confirm the accuracy of the driven statistical analytical approach but also prove the robustness of the proposed technique against common signal processing attacks

Modeling and frequency tracking of marine mammal whistle calls

Submitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2009Marine mammal whistle calls present an attractive medium for covert underwater communications. High quality models of the whistle calls are needed in order to synthesize natural-sounding whistles with embedded information. Since the whistle calls are composed of frequency modulated harmonic tones, they are best modeled as a weighted superposition of harmonically related sinusoids. Previous research with bottlenose dolphin whistle calls has produced synthetic whistles that sound too “clean” for use in a covert communications system. Due to the sensitivity of the human auditory system, watermarking schemes that slightly modify the fundamental frequency contour have good potential for producing natural-sounding whistles embedded with retrievable watermarks. Structured total least squares is used with linear prediction analysis to track the time-varying fundamental frequency and harmonic amplitude contours throughout a whistle call. Simulation and experimental results demonstrate the capability to accurately model bottlenose dolphin whistle calls and retrieve embedded information from watermarked synthetic whistle calls. Different fundamental frequency watermarking schemes are proposed based on their ability to produce natural sounding synthetic whistles and yield suitable watermark detection and retrieval

Extracting Source Level Program Similarities from Dynamic Behavior

The vast majority of work on comparing program similarities to detect software piracy either assumes the availability of the program source code (e.g., Moss) or performs a complicated source program transformation to embed carefully designed signatures, or software watermarks, into the binary code. In this paper, we propose a new approach to detecting program similarities that requires neither the availability of the program source nor complicated compile-time watermarking techniques. Furthermore, in contrast to the alternatives, our framework is resistant to standard attacks such as code obfuscation. Our approach exploits the observation that the sequence of system calls performed by a program execution provides a strong signature of the program semantics or functionality, thereby using the inherent properties of a program to identify it. By statistically analyzing sequences of system calls, the relative similarities and differences of program regions can be automatically determined. We have developed a framework that automatically extracts system call sequences, computes the similarities between two binaries via statistical analysis, and maps dynamically similar regions onto textually similar source files. We present several case studies showing the applicability of our framework in pinpointing pirated segments. Our experimental study also shows that directly comparing the binary files of the programs without considering their dynamic behavior is ineffective, and demonstrates strong consistency between the output of our new framework and that of Moss