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In this paper we present a reversible watermarking technique for digital images. In our context, reversibility refers to the ability to restore the original image by the watermark detector. The technique is based on a transformation function that introduces 'gaps' in the image histogram. The 'gaps' are used to encode the watermark. Several experiments revealed that a relatively high embedding rate may be achieved. For a set of test images we obtained embedding rates between 0.06 and 0.6 bits per pixel at PSNR levels of 45-50. INTRODUCTlONIn the last few years we see an increasing interest in reversible watermarking methods. In this context, "reversihility" means the ability to repair the original input data at the decoder. In summary any reversibly watermarking system comprises the following steps: (i) Embedding a digital watermxk signal w in a digital host signal x resulting in y = x + w, (ii) transmitting the watermarked signal y from the encoder to the decoder through an error-free transmission channel, and (iii) extracting the watermark signal w and restoring the original host x = y -w.We are aware of several techniques for reversible data embedding. To the best of our knowledge, a concept for reversible embedding first appeared in an authentication method for images in a patent from the East" Kodak Company 111. Inspired by this work, several research groups published a number of papers on the same subject [2] [3] [4]. These methods are dedicated to digital images. In [5] we introduced a reversible watermarking technique for digital audio signals. It is based on companding such as used in many telephony systems [61 and provides an enormous data embedding rate.In this paper we adapt this companding-based reversible watermarking technique to digital images. By generalizing this methodology we introduce a new class of reversible watermarking techniques for digital images. In section 2 we briefly recall the basic principles of [5] and present the generalized embedding and detection techniques. In the remainder of the paper we show examples in which we apply the technique to images. REVERSIBLE WATERMARKING TECHNIQUEIn this section we recall the basic concepts of the compandingbased reversible watermarking method described in [5]. The encoding and decoding strategies are displayed in Figure 1. Assume we have a time-discrete signal x of length N , with 3: E { O , l , . . . ,2" -l}N = NZ, and where m E N indicates the number of bits used to represent each sample. The host signal x is passed through a compression function CQ in the following manner:where X Q represents the mapped signal. The purpose of this operation is to map input samples x[n] to unique output samples ZQ[n]. In [5] we showed that a simple bit-shift operation (i.e. 1 maps to 2,2 maps to 4 , 3 maps to 6, etc.) provided the basis for a good compression function of digital audio signals. The idea is that sample values that were not used by the mapping functions (i.e. all the odd-valued samples in the bit-shift example) are deployed for embedding data d....

Based on existing technology used in image and video watermarking, we have developed a robust audio watermarking technique. The embedding algorithm operates in frequency domain, where the magnitudes of the Fourier coefficients are slightly modified. In the temporal domain, an additional scale parameter and gain function are necessary to refine the watermark and achieve perceptual transparency. Watermark detection relies on the Symmetrical Phase Only Matched Filtering (SPOMF) cross-correlation approach. Not only the presence of a watermark, but also its cyclic shift is detected. This shift supports a multi-bit payload for one particular watermark sequence. The watermarking technology proved to be very robust to a large number of signal processing "attacks" such as MP3 (64 kb/s), all-pass filtering, echo addition, time-scale modification, resampling, noise addition, etc. It is expected that this approach may contribute in a wide variety of existing (e.g. monitoring and copy protection) and future applications.

The automatic recognition of child activity using multisensor data enables various applications such as childdevelopment monitoring, energy-expenditure estimation, child-obesity prevention, child safety in and around the home, etc. We formulate the activity recognition task as a classification problem based on multiple sensors embedded in a wearable device. The approach we propose in this paper is to apply spectral analysis techniques of multiple sensor data for activity recognition. Quadratic Discriminant Analysis (QDA) classifier is then trained using manually annotated data and applied for activity recognition. The obtained experimental results for the recognition of 7 activities based on a limited data set are promising and show the potential of the proposed method.