Mohamed F. Mansour

Audio watermarking by time-scale modification

A new algorithm for audio watermarking is proposed. The basic idea of the algorithm is to change the length of the intervals between salient points of the audio signal to embed data. We propose several novel ideas for practical implementations that can be used by other watermarking schemes as well. The algorithm is robust to common audio processing operations e.g. MP3 lossy compression, low pass filtering, and time-scale modification. The watermarked signal has very high perceptual quality and is indistinguishable from the original signal.

Time-scale invariant audio data embedding

We propose a novel algorithm for high-quality data embedding in audio. The algorithm is based on changing the relative length of the middle segment between two successive maximum and minimum peaks to embed data. Spline interpolation is used to change the lengths. To ensure smooth monotonic behavior between peaks, a hybrid orthogonal and nonorthogonal wavelet decomposition is used prior to data embedding. The possible data embedding rates are between 20 and 30 bps. However, for practical purposes, we use repetition codes, and the effective embedding data rate is around 5 bps. The algorithm is invariant after time-scale modification, time shift, and time cropping. It gives high-quality output and is robust to mp3 compression.

LMS-based attack on watermark public detectors

We describe a generalized attack on image watermarking schemes when the decoder is publicly available. The attack applies to most common watermarking schemes. In particular, we describe in detail the implementation of the attack against correlator-based watermark detectors using the least mean square (LMS) algorithm. We establish the effectiveness of the attack in removing the watermark with least distortion. Also, we describe a counterattack to avoid this problem by using nonparametric decision boundaries at the detector.

Efficient decoding of watermarking schemes in the presence of false alarms

We treat the problem of recovering the correct message if extra bits (false alarms) are added to the body of the message at random locations. This situation is common for some watermarking systems with selective embedding which is typical when the human visual (or audio) system is incorporated to enhance the quality of the watermarking system. We propose an efficient decoding scheme for convolutional codes using some modifications of the Viterbi algorithm. Extra states are added to represent the possible false alarms and a new expansion algorithm of the modified trellis diagram is proposed. The simulation results show the high efficiency of our algorithm in detecting random false alarms with high rates.

Techniques for data embedding in image using wavelet extrema

In this work, we propose new multiresolution techniques for data embedding in imagery. The wavelet extrema of the image are exploited to embed data. We use the wavelet extrema of the dyadic non-orthogonal wavelet transform. These extrema represent high frequency points in the image, hence modifications in their neighborhoods have minor visual distortion.

Convolutional decoding for channels with false alarms

In this work, we propose a new channel model that is suited for systems with irregular sampling, e.g. selective data embedding in digital media. The channel model accounts for the possible occurrence of false alarms, i.e. extra data bits, in the received sequence. We propose modifications to the common decoding schemes of the convolutional codes namely, the Viterbi and the sequential decoding to compensate for these false alarms. The simulation results establish the effectiveness of the proposed algorithms in detecting false alarms with high rates.

Secure watermark detection with nonparametric decision boundaries

In this paper we will address the problem of constructing a nonparameteric decision boundary for watermark detection. Most current watermarking algorithms have a parametric decision boundary that can be undone if the pirate has unlimited access to the detector. In this work we propose a fractal decision boundary which can not be estimated. That boundary is obtained by processing the decision boundary corresponding to the underlying watermarking algorithm. The performance of the new technique is essentially similar to any watermarking algorithm from which it is derived.

Attacks on quantization-based watermarking schemes

In this work, we introduce an efficient attack for removing the watermark with minimum distortion from quantization-based watermarking schemes. The attack is based on analyzing the quantization error to extract the key parameters of the encoder. The removal of the watermark is done by perturbing enough components of the quantized watermarked signal by half of the estimated quantization step. Simulation experiments show the high effectiveness of the proposed algorithm in hacking known quantization-based schemes for watermarking.

An improved error control paradigm for multimedia transmission over wireless networks

Providing quality-of-service (QoS) guarantees over wireless packet networks requires a thorough understanding and quantification of the interactions among the traffic source, the wireless channel characteristics, the underlying link-layer error control mechanisms, and the various packet dropping policies adopted by the upper network layers. We explore the idea of cooperation among some of these different aspects to assist the packet level error recovery. First, we revise UDP to tolerate a certain amount of channel error and to allow the delivery of partially corrupted packets. Secondly, we propose a novel technique to resolve the problem of missed bits without the need to identify their exact locations. It is based on modifying the stack algorithm for convolutional decoding. Simulation results establish the effectiveness of the proposed approach for detecting and correcting missed bits, which have direct impact on packet loss and on the overall system performance.

Convolutional codes for channels with substitutions, insertions, and deletions

We introduce modifications to common convolutional decoders to accommodate channels with Insertions and deletions. For the Viterbi decoder, new states are added to the trellis diagram to represent the new situation and the expansion algorithm is modified accordingly. For sequential decoding, we provide modifications to the stack algorithm and a new metric is introduced. Also, we developed a systematic way for convolutional code construction using Simulated Annealing so as to maximize the distance between codewords In the presence of insertions and deletions. The proposed techniques are shown to be superior to previous approaches for this problem, and have no additional code overhead.