This paper reports on the implementation of the Digimarc® Discover platform on Google Glass, enabling the reading of a watermark embedded in a printed material or audio. The embedded watermark typically contains a unique code that identifies the containing media or object and a synchronization signal that allows the watermark to be read robustly. The Digimarc Discover smartphone application can read the watermark from a small portion of printed image presented at any orientation or reasonable distance. Likewise, Discover can read the recently introduced Digimarc Barcode to identify and manage consumer packaged goods in the retail channel. The Digimarc Barcode has several advantages over the traditional barcode and is expected to save the retail industry millions of dollars when deployed at scale. Discover can also read an audio watermark from ambient audio captured using a microphone. The Digimarc Discover platform has been widely deployed on the iPad, iPhone and many Android-based devices, but it has not yet been implemented on a head-worn wearable device, such as Google Glass. Implementing Discover on Google Glass is a challenging task due to the current hardware and software limitations of the device. This paper identifies the challenges encountered in porting Discover to the Google Glass and reports on the solutions created to deliver a prototype implementation.
This paper presents a speed comparison between the use of Digimarc® Barcodes and the Universal Product Code (UPC) for customer checkout at point of sale (POS). The recently introduced Digimarc Barcode promises to increase the speed of scanning packaged goods at POS. When this increase is exploited by workforce optimization systems, the retail industry could potentially save billions of dollars. The Digimarc Barcode is based on Digimarc’s watermarking technology, and it is imperceptible, very robust, and does not require any special ink, material, or printing processes. Using an image-based scanner, a checker can quickly scan consumer packaged goods (CPG) embedded with the Digimarc Barcode without the need to reorient the packages with respect to the scanner. Faster scanning of packages saves money and enhances customer satisfaction. It reduces the length of the queues at checkout, reduces the cost of cashier labor, and makes self-checkout more convenient. This paper quantifies the increase in POS scanning rates resulting from the use of the Digimarc Barcode versus the traditional UPC. It explains the testing methodology, describes the experimental setup, and analyzes the obtained results. It concludes that the Digimarc Barcode increases number of items per minute (IPM) scanned at least 50% over traditional UPC.
In this paper, we improve the performance of the hierarchical detector we proposed in [1] for real-time software or low-cost hardware implementation. Although the original hierarchical detector is faster than sub-sampled brute force-base detector when processing marked images, it unnecessarily continues to process unmarked images looking for a watermark that is not present. This processing is time-consuming; hence, it represents a significant deployment obstacle. The improved detector, however, avoids most of the processing of the unmarked areas of an image by exploiting the presence of a reference signal usually included with the embedded watermark. This reference signal enables the detector to synchronize the image after it has been subjected to a geometric transformation (scaling, rotation, and translation). The improved detector refrains from searching an image area any further whenever the level of the reference signal is very weak or the estimated scale factors and rotation angles associated with this reference signal are not consistent among the processed blocks within the same layer in the hierarchy. The proposed detector has been implemented, and the experimental results indicate that the proposed detector is computationally more efficient with unmarked images, while achieving a detection rate similar to that of the original hierarchical detector.
KEYWORDS: Digital watermarking, Data hiding, Signal processing, Distortion, Data communications, Algorithm development, Databases, Computer programming, Data conversion, Computer simulations
A high-capacity, data-hiding algorithm that lets the user embed a large amount of data in a digital audio signal is presented in this paper. The algorithm also lets the user restore the original digital audio from the watermarked digital audio after retrieving the hidden data. The hidden information can be used to authenticate the audio, communicate copyright information, facilitate audio database indexing and information retrieval without degrading the quality of the original audio signal, or enhance the information content of the audio. It also allows secret communication between two parties over a digital communication link. The proposed algorithm is based on a generalized, reversible, integer transform, which calculates the average and pair-wise differences between the elements of a vector composed from the audio samples. The watermark is embedded into the pair-wise difference coefficients of selected vectors by replacing their least significant bits (LSB) with watermark bits. Most of these coefficients are shifted left by one bit before replacing their LSB. The vectors are carefully selected such that they remain identifiable after embedding and they do not suffer from overflow or underflow after embedding. To ensure reversibility, the locations of the shifted coefficients and the original LSBs are appended to the payload. Simulation results of the algorithm and its performance are presented and discussed in the paper.
A high-capacity, data-hiding algorithm that lets the user restore the original host image after retrieving the hidden data is presented in this paper. The proposed algorithm can be used for watermarking valuable or sensitive images such as original art works or military and medical images. The proposed algorithm is based on a generalized, reversible, integer transform, which calculates the average and pair-wise differences between the elements of a vector extracted from the pixels of the image. The watermark is embedded into a set of carefully selected coefficients by replacing the least significant bit (LSB) of every selected coefficient by a watermark bit. Most of these coefficients are shifted left by one bit before replacing their LSBs. Several conditions are derived and used in selecting the appropriate coefficients to ensure that they remain identifiable after embedding. In addition, the selection of coefficients ensures that the embedding process does not cause any overflow or underflow when the inverse of the transform is computed. To ensure reversibility, the locations of the shifted coefficients and the original LSBs are embedded in the selected coefficients before embedding the desired payload. Simulation results of the algorithm and its performance are also presented and discussed in the paper.
In this paper, we propose a new method for watermarking electronic text documents that contain justified paragraphs and irregular line spacing. The proposed method uses a spread-spectrum technique to combat the effects of irregular word or line spacing. It also uses a BCH (Bose-Chaudhuri-Hocquenghem) error coding technique to protect the payload from the noise resulting from the printing and scanning process. Watermark embedding in a justified paragraph is achieved by slightly increasing or decreasing the spaces between words according to the value of the corresponding watermark bit. Similarly, watermark embedding in a text document with variable line spacing is achieved by slightly increasing or decreasing the distance between any two adjacent lines according to the value of the watermark bit. Detecting the watermark is achieved by measuring the spaces between the words or the lines and correlating them with the spreading sequence. In this paper, we present an implementation of the proposed algorithm and discuss its simulation results.
A novel watermarking algorithm for watermarking low bit-rate MPEG-4 compressed video is developed and evaluated in this paper. Spatial spread spectrum is used to invisibly embed the watermark into the host video. A master synchronization template is also used to combat geometrical distortion such as cropping, scaling, and rotation. The same master synchronization template is used for watermarking all video objects (VOP) in the bit-stream, but each object can be watermarked with a unique payload. A gain control algorithm is used to adjust the local gain of the watermark, in order to maximize watermark robustness and minimize the impact on the quality of the video. A spatial and temporal drift compensator is used to eliminate watermark self-interference and the drift in quality due to AC/DC prediction in I-VOPs and motion compensation in P- and B-VOPs, respectively. Finally, a bit-rate controller is used to maintain the data-rate at an acceptable level after embedding the watermark. The developed watermarking algorithm is tested using several bit-streams at bit-rates ranging from 128-750 Kbit/s. The visibility and the robustness of the watermark after decompression, rotation, scaling, sharpening, noise reduction, and trans-coding are evaluated.
KEYWORDS: Digital watermarking, Cameras, Internet, Sensors, Image processing, Signal processing, Signal detection, Image quality, Scanners, Control systems
This paper introduces the concept of Smart Images and explains the use of watermarking technology in their implementation. A Smart Image is a digital or physical image that contains a digital watermark, which leads to further information about the image content via the Internet, communicates ownership rights and the procedure for obtaining usage rights, facilitates commerce, or instructs and controls other computer software or hardware. Thus, Smart Images, empowered by digital watermarking technology, act as active agents or catalysts which gracefully bridge both traditional and modern electronic commerce. This paper presents the use of Digimarc Corporation's watermarking technology to implement Smart Images. The paper presents an application that demonstrates how Smart Images facilitate both traditional and electronic commerce. The paper also analyzes the technological challenges to be faced for ubiquitous use of Smart Images.
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