Official figures in Africa indicate that 1,349 rhinos were killed in 2015. This marked the most critical moment of the current rhino poaching crisis that began in 2008. This trend has since reversed to the minimum of 1,124 poached rhinos achieved in 2017. Although this brings hope, this emergency is still a formidable challenge that needs to be tackled from a multi-angle approach including anti-poaching collaboration among countries to enforce effective wildlife crime laws, target campaigns in the illegal horn rhino end-user countries like China and Vietnam, and the adoption of cutting-edge technology by governmental agencies and conservationist NGOs.

A novel complex flat fading channel estimation scheme is proposed. Contrarily to previous schemes in the literature, this new approach is not based on introducing pilot sequences, but on reducing the interference caused by the information-bearing signal on the estimation-aiding signal by using Dirty Paper Coding. We show through simulations that our method outperforms the Partially-Data Dependent scheme, which is a state-of-the-art technique based on superimposed pilots.

Watermarking schemes based on the Dirty Paper Coding (DPC) paradigm have been shown to achieve much higher rates than classical Spread-Spectrum methods. However, in practice, the latter continue to be used due to their higher security and robustness. In fact, the most prevalent DPC method, the so-called Scalar Costa Scheme (SCS), is prone to non-additive attacks, such as a simple gain which produces a desynchronization between the embedding and decoding codebooks thus severely affecting performance. Although some gain-robust modifications to the basic SCS exist, all have serious drawbacks. One alternative, which was somehow abandoned for its complexity, is to estimate the gain at the decoder, with the advantage of preserving the simplicity of SCS. In this paper we take a new look at the estimation problem and propose an affordable algorithm to perform Maximum Likelihood estimation of the channel gain, that is able to restore the original SCS performance. We also show and experimentally illustrate how our scheme can be effectively adapted to watermark decoding in filtered images.

Wireless multimedia sensor networks (WMSNs) emerge as a solution in wireless unattended video surveillance scenarios. The G-E-M methodology was recently introduced by Czarlinska et al. to address the problem providing protection to visual wireless surveillance systems in the presence of another hostile sensing system. This work builds upon the G-E-M framework by introducing pre- and post-processing stages that reduce decoding errors and visual noise as well as allowing the effective control of bitrate.

A performance analysis of the authentication method proposed by J. Fridrich and M. Goljan is carried out. This method has the particular feature that both the embedder and the detector generate the watermark from a perceptual digest of the image. Hence, in order to accurately analyze the performance, the digest errors caused by the watermark embedding, the addition of a complementary signal and the scaling attacks are also taken into account.

This paper analyzes the performance of the image authentication method based on robust hashing proposed by J. Fridrich and M. Goljan . In this method, both the embedder and the detector generate the watermark from a perceptual digest of the image. Therefore, an accurate performance analysis requires assessing the relation between noise and hash bit errors. Our approach first derives the probability of hash bit error due to watermark embedding and/or the attack, and then uses such probability to derive the probabilities of false positive and false negative.