155 research outputs found

    Deep Intellectual Property: A Survey

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    With the widespread application in industrial manufacturing and commercial services, well-trained deep neural networks (DNNs) are becoming increasingly valuable and crucial assets due to the tremendous training cost and excellent generalization performance. These trained models can be utilized by users without much expert knowledge benefiting from the emerging ''Machine Learning as a Service'' (MLaaS) paradigm. However, this paradigm also exposes the expensive models to various potential threats like model stealing and abuse. As an urgent requirement to defend against these threats, Deep Intellectual Property (DeepIP), to protect private training data, painstakingly-tuned hyperparameters, or costly learned model weights, has been the consensus of both industry and academia. To this end, numerous approaches have been proposed to achieve this goal in recent years, especially to prevent or discover model stealing and unauthorized redistribution. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: challenges/threats, invasive solutions (watermarking), non-invasive solutions (fingerprinting), evaluation metrics, and performance. We finish the survey by identifying promising directions for future research.Comment: 38 pages, 12 figure

    Generative Model Watermarking Based on Human Visual System

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    Intellectual property protection of deep neural networks is receiving attention from more and more researchers, and the latest research applies model watermarking to generative models for image processing. However, the existing watermarking methods designed for generative models do not take into account the effects of different channels of sample images on watermarking. As a result, the watermarking performance is still limited. To tackle this problem, in this paper, we first analyze the effects of embedding watermark information on different channels. Then, based on the characteristics of human visual system (HVS), we introduce two HVS-based generative model watermarking methods, which are realized in RGB color space and YUV color space respectively. In RGB color space, the watermark is embedded into the R and B channels based on the fact that HVS is more sensitive to G channel. In YUV color space, the watermark is embedded into the DCT domain of U and V channels based on the fact that HVS is more sensitive to brightness changes. Experimental results demonstrate the effectiveness of the proposed work, which improves the fidelity of the model to be protected and has good universality compared with previous methods.Comment: https://scholar.google.com/citations?user=IdiF7M0AAAAJ&hl=e

    A HIGH SPEED VLSI ARCHITECTURE FOR DIGITAL SPEECH WATERMARKING WITH COMPRESSION

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    The need to provide a copy right protection on digital watermarking to multimedia data like speech, image or video is rapidly increasing with an intensification in the application in these areas. Digital watermarking has received a lot of attention in the past few years. A hardware system based solely on DSP processors are fast but may require more area, cost or power if the target application requires a large amount of parallel processing. An FPGA co-processor can provide as many as 550 parallel multiply and accumulate operations on a single device, but FPGAs excel at processing large amounts of data in parallel, as they are not optimized as processors for tasks such as periodic coefficient updates, decision- making control tasks. Combination of both the FPGA and DSP processor delivers an attractive solution for a wide range of applications. A hardware implementation of digital speech watermarking combined with speech compression, encryption on heterogeneous platform is made in this paper. It is observed that the proposed architecture is able to attain high speed while utilizing optimal resources in terms of area

    A Secured Joint Encrypted Watermarking In Medical Image Using Block Cipher Algorithm

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    ABSTRACT At present year, most of the hospitals and diagnostic centre have exchanging the biomedical information through wireless media. reliability of the information can be verified by adding ownership data as the watermarking and encryption in the original information. In our proposed work, a joint encryption/watermarking system for the purpose of protecting medical image. This system based on approach which combines a substitutive watermarking algorithm with an encryption algorithm, advanced encryption standard (AES) in counter mode. If the watermarking and encryption are conducted jointly at the protection stage, watermark extraction and decryption can be applied independently. The capability of our system to securely make available security attributes in encrypted domains while minimizing the elapsed time. Furthermore, by making use of the AES algorithm in counter (CTR) mode make our compliant with the DICOM (Digital Imaging and Communications in Medicine) standard

    Artificial Fingerprinting for Generative Models: {R}ooting Deepfake Attribution in Training Data

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    Data Hiding and Its Applications

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    Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others
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