A watermark is an invisible mark placed on an image that can be detected when the image is compared with the original. The mark is designed to identify both the source of an image as well as its intended recipient. The mark should be tolerant to reasonable quality lossy compression of the image using transform coding or vector quantization. Standard image processing operations such as low pass filtering, cropping, translation and rescaling should not remove the mark. Spread spectrum communication techniques and matrix transformations can be used together to design watermarks that are robust to tampering and are visually imperceptible. This paper discusses techniques for embedding such marks in grey scale digital images. It also proposes a novel phase based method of conveying the watermark information. In addition, the use of optimal detectors for watermark identification is also proposed

A watermark is an invisible mark placed on an image that is designed to identify both the source of an image as well as its intended recipient. The authors present an overview of watermarking techniques and demonstrate a solution to one of the key problems in image watermarking, namely how to hide robust invisible labels inside grey scale or colour digital images

Abstract:A watermark is an invisible mark placed on an image that is designed to identify both the source of an image as well as its intended recipient. The authors present an overview of watermarking techniques and demonstrate a solution to one of the key problems in image watermarking, namely how to hide robust invisible labels inside grey scale or colour digital images.

No abstract

No abstract

The human ability to localize sound is essential for monitoring our environment and helps us to analyse complex auditory scenes. Although the acoustic cues mediating sound localization have been established, it remains unknown how these cues are represented in human cortex. In particular, it is still a point of contention whether binaural and monaural cues are processed by the same or distinct cortical networks. In this study, participants listened to a sequence of auditory stimuli from different spatial locations while we recorded their neural activity using electroencephalography (EEG). The stimuli were presented over a loudspeaker array, which allowed us to deliver realistic, free-field stimuli in both the horizontal and vertical planes. Using a multivariate classification approach, we showed that it is possible to decode sound source location from scalp-recorded EEG. Robust and consistent decoding was shown for stimuli that provide binaural cues (i.e. Left vs. Right stimuli). Decoding location when only monaural cues were available (i.e. Front vs. Rear and elevational stimuli) was successful for a subset of subjects and showed less consistency. Notably, the spatio-temporal pattern of EEG features that facilitated decoding differed based on the availability of binaural and monaural cues. In particular, we identified neural processing of binaural cues at around 120 ms post-stimulus and found that monaural cues are processed later between 150 and 200 ms. Furthermore, different spatial activation patterns emerged for binaural and monaural cue processing. These spatio-temporal dissimilarities suggest the involvement of separate cortical mechanisms in monaural and binaural acoustic cue processing.

No abstract

No abstract