The present invention provides a method and apparatus for the production and labeling of objects in a manner suitable for the prevention and detection of counterfeiting. Thus, the system incorporates a variety of features that make unauthorized reproduction difficult. In addition, the present invention provides an efficient means for the production of labels and verification of authenticity, whereby a recording apparatus which includes a recording medium, having anisotrophic optical domains, along with a means for transferring a portion of the recording medium to a carrier, wherein a bulk portion of the recording medium has macroscopically detectable anisotrophic optical properties and the detecting apparatus thereon.

Authentication method and system

Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM. This project page is at this https URL this https URL

Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules

Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM.

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Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules

Impacts of increasing volume of digital forensic data

A system for issuing a dynamic temporary credential to a portable communication device for use in a transaction with an electronic control point. The system receives the current geo-location of the portable communication device and transmits a dynamic temporary credential to the portable communication device from the centralized computer. The system further scores the risk in authorizing a transaction associated with an electronic control point using the dynamic temporary credential it issued. The system may prevent the transmission of the dynamic temporary credential until the end user has been authenticated, which may include verifying one or more of a manually input passcode, the unique digital signature of the portable communication device, and know your customer queries. The system may further include a validation mapping gateway operably connected to one or more issuers that substitutes legacy payment data for the dynamic temporary credential in a payment transaction before sending the payment transaction along with the risk score to the issuer associated with the legacy payment data.

System and method for dynamic temporary payment authorization in a portable communication device

We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit allocation is required. Based on these contexts, we allow the model to more accurately estimate the distribution of each latent representation with a more generalized form of the approximation models, which accordingly leads to an enhanced compression performance. Based on the experimental results, the proposed method outperforms the traditional image codecs, such as BPG and JPEG2000, as well as other previous artificial-neural-network (ANN) based approaches, in terms of the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity (MS-SSIM) index.

Context-adaptive Entropy Model for End-to-end Optimized Image Compression

Recently, learned image compression methods have been actively studied. Among them, entropy-minimization based approaches have achieved superior results compared to conventional image codecs such as BPG and JPEG2000. However, the quality enhancement and rate-minimization are conflictively coupled in the process of image compression. That is, maintaining high image quality entails less compression and vice versa. However, by jointly training separate quality enhancement in conjunction with image compression, the coding efficiency can be improved. In this paper, we propose a novel joint learning scheme of image compression and quality enhancement, called JointIQ-Net, as well as entropy model improvement, thus achieving significantly improved coding efficiency against the previous methods. Our proposed JointIQ-Net combines an image compression sub-network and a quality enhancement sub-network in a cascade, both of which are end-to-end trained in a combined manner within the JointIQ-Net. Also the JointIQ-Net benefits from improved entropy-minimization that newly adopts a Gussian Mixture Model (GMM) and further exploits global context to estimate the probabilities of latent representations. In order to show the effectiveness of our proposed JointIQ-Net, extensive experiments have been performed, and showed that the JointIQ-Net achieves a remarkable performance improvement in coding efficiency in terms of both PSNR and MS-SSIM, compared to the previous learned image compression methods and the conventional codecs such as VVC Intra (VTM 7.1), BPG, and JPEG2000. To the best of our knowledge, this is the first end-to-end optimized image compression method that outperforms VTM 7.1 (Intra), the latest reference software of the VVC standard, in terms of the PSNR and MS-SSIM.

An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization

Cloud computing has become one of mobile's hottest topics. Moving computing processing and data storage away from mobile devices and into large data centers, mobile applications enable the users to improve productivity, to share data and to collaborate with others. Considering the benefits of mobile cloud computing, the forensic service based on mobile cloud computing could be good solution to the problems today's forensic tools are facing. Therefore, this paper introduces a service concept called i®Forensic Cloud' to develop new paradigm in digital forensics. Furthermore, in order to show the feasibility of the concept, the paper suggests a technology framework for forensic analysis based on mobile cloud and describe current status of its development.

Pervasive Forensic Analysis Based on Mobile Cloud Computing

A remote forensics system based on a network is provided to allow for accessing a forensics analysis center from a remote area to perform forensic analysis. The network-based remote forensic system includes: one or more remote terminals performing forensic analysis on an evidence device in a remote area, through a virtual forensic tool when the evidence device is connected thereto; and an investigation center system connected to the remote terminals via a wide area network to provide the virtual forensic tool, processing a requirement of the remote terminals, and providing requirement processing results to the remote terminals.

Jooyoung Lee

Papers

Context-adaptive Entropy Model for End-to-end Optimized Image Compression

An End-to-End Joint Learning Scheme of Image Compression and Quality Enhancement with Improved Entropy Minimization

Pervasive Forensic Analysis Based on Mobile Cloud Computing

Context-adaptive Entropy Model for End-to-end Optimized Image Compression

Remote forensics system based on network