255 research outputs found
PCPT and ACPT: Copyright Protection and Traceability Scheme for DNN Models
Deep neural networks (DNNs) have achieved tremendous success in artificial
intelligence (AI) fields. However, DNN models can be easily illegally copied,
redistributed, or abused by criminals, seriously damaging the interests of
model inventors. The copyright protection of DNN models by neural network
watermarking has been studied, but the establishment of a traceability
mechanism for determining the authorized users of a leaked model is a new
problem driven by the demand for AI services. Because the existing traceability
mechanisms are used for models without watermarks, a small number of
false-positives are generated. Existing black-box active protection schemes
have loose authorization control and are vulnerable to forgery attacks.
Therefore, based on the idea of black-box neural network watermarking with the
video framing and image perceptual hash algorithm, a passive copyright
protection and traceability framework PCPT is proposed that uses an additional
class of DNN models, improving the existing traceability mechanism that yields
a small number of false-positives. Based on an authorization control strategy
and image perceptual hash algorithm, a DNN model active copyright protection
and traceability framework ACPT is proposed. This framework uses the
authorization control center constructed by the detector and verifier. This
approach realizes stricter authorization control, which establishes a strong
connection between users and model owners, improves the framework security, and
supports traceability verification
Discrete Wavelet Transforms
The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications
Protection of Relational Databases by Means of Watermarking: Recent Advances and Challenges
Databases represent today great economical and strategic concerns for both enterprises and public institutions. In that context, where data leaks, robbery as well as innocent or even hostile data degradation represent a real danger, and watermarking appears as an interesting tool. Watermarking is based on the imperceptible embedding of a message or watermark into a database in order, for instance, to determine its origin as well as to detect if it has been modified. A major advantage of watermarking in relation to other digital content protection mechanisms is that it leaves access to the data while keeping them protected by means of a watermark, independent of the data format storage. Nevertheless, it is necessary to ensure that the introduced distortion does not perturb the exploitation of the database. In this chapter, we give a general overview of the latest database watermarking methods, focusing on those dealing with distortion control. In particular, we present a recent technique based on an ontological modeling of the database semantics that represent the relationships in between attributes—relationships that should be preserved in order to avoid the appearance of incoherent and unlikely records
Adaptive Reversible Data Hiding Scheme for Digital Images Based on Histogram Shifting
Existing histogram based reversible data hiding schemes use only absolute difference values between the neighboring pixels of a cover image. In these schemes, maxima and minima points at maximum distance are selected in all the blocks of the image which causes shifting of the large number of pixels to embed the secret data. This shifting produces more degradation in the visual quality of the marked image. In this work, the cover image is segmented into blocks, which are classified further into complex and smooth blocks using a threshold value. This threshold value is optimized using firefly algorithm. Simple difference values between the neighboring pixels of complex blocks have been utilized to embed the secret data bits. The closest maxima and minima points in the histogram of the difference blocks are selected so that number of shifted pixels get reduced, which further reduces the distortion in the marked image. Experimental results prove that the proposed scheme has better performance as compared to the existing schemes. The scheme shows minimum distortion and large embedding capacity. Novelty of work is the usage of negative difference values of complex blocks for secret data embedding with the minimal number of pixel shifting
Implementation of Lossless Preprocessing Technique for Student Record System
The Implementation of Lossless Preprocessing Technique for Student Record System of the Lyceum of the Philippines University mainly aims to apply an effective data compression algorithm to efficiently store data, improve data transmission via web-based infrastructure and ensure security of records. The system is comprised of the following modules namely: Smart ID Registration Module, Client-Side Application Module and Information Kiosk Module. For enhanced security consideration, GZIPalso known as GNU ZIP algorithm is implemented to compress records before saving in the central database. It is a lossless data compression utility that is based on the deflate algorithm with the format defined in Internet Engineering Task Force RFC1951: DEFLATE Compressed Data Format Specification version 1.32. This standard references the use of the LZ77 (Lempil-Ziv, 1977) compression algorithm combined with Huffman coding. On the other hand, the lossless decompressionrestores the data by bringing back the removed redundancy and produces an exact replica of the original source data. Results of the software evaluation indicate high acceptability on the overall system performance
Mapping Stream Programs into the Compressed Domain
Due to the high data rates involved in audio, video, and signalprocessing applications, it is imperative to compress the data todecrease the amount of storage used. Unfortunately, this implies thatany program operating on the data needs to be wrapped by adecompression and re-compression stage. Re-compression can incursignificant computational overhead, while decompression swamps theapplication with the original volume of data.In this paper, we present a program transformation that greatlyaccelerates the processing of compressible data. Given a program thatoperates on uncompressed data, we output an equivalent program thatoperates directly on the compressed format. Our transformationapplies to stream programs, a restricted but useful class ofapplications with regular communication and computation patterns. Ourformulation is based on LZ77, a lossless compression algorithm that isutilized by ZIP and fully encapsulates common formats such as AppleAnimation, Microsoft RLE, and Targa.We implemented a simple subset of our techniques in the StreamItcompiler, which emits executable plugins for two popular video editingtools: MEncoder and Blender. For common operations such as coloradjustment and video compositing, mapping into the compressed domainoffers a speedup roughly proportional to the overall compressionratio. For our benchmark suite of 12 videos in Apple Animationformat, speedups range from 1.1x to 471x, with a median of 15x
Wearable Wireless Devices
No abstract available
Error resilient image transmission using T-codes and edge-embedding
Current image communication applications involve image transmission over noisy channels, where the image gets damaged. The loss of synchronization at the decoder due to these errors increases the damage in the reconstructed image. Our main goal in this research is to develop an algorithm that has the capability to detect errors, achieve synchronization and conceal errors.;In this thesis we studied the performance of T-codes in comparison with Huffman codes. We develop an algorithm for the selection of best T-code set. We have shown that T-codes exhibit better synchronization properties when compared to Huffman Codes. In this work we developed an algorithm that extracts edge patterns from each 8x8 block, classifies edge patterns into different classes. In this research we also propose a novel scrambling algorithm to hide edge pattern of a block into neighboring 8x8 blocks of the image. This scrambled hidden data is used in the detection of errors and concealment of errors. We also develop an algorithm to protect the hidden data from getting damaged in the course of transmission
Towards Deep Network Steganography: From Networks to Networks
With the widespread applications of the deep neural network (DNN), how to
covertly transmit the DNN models in public channels brings us the attention,
especially for those trained for secret-learning tasks. In this paper, we
propose deep network steganography for the covert communication of DNN models.
Unlike the existing steganography schemes which focus on the subtle
modification of the cover data to accommodate the secrets, our scheme is
learning task oriented, where the learning task of the secret DNN model (termed
as secret-learning task) is disguised into another ordinary learning task
conducted in a stego DNN model (termed as stego-learning task). To this end, we
propose a gradient-based filter insertion scheme to insert interference filters
into the important positions in the secret DNN model to form a stego DNN model.
These positions are then embedded into the stego DNN model using a key by side
information hiding. Finally, we activate the interference filters by a partial
optimization strategy, such that the generated stego DNN model works on the
stego-learning task. We conduct the experiments on both the intra-task
steganography and inter-task steganography (i.e., the secret and stego-learning
tasks belong to the same and different categories), both of which demonstrate
the effectiveness of our proposed method for covert communication of DNN
models.Comment: 8 pages. arXiv admin note: text overlap with arXiv:2302.1452
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