88 research outputs found
Study and Implementation of Watermarking Algorithms
Water Making is the process of embedding data called a watermark into a multimedia object such that watermark can be detected or extracted later to make an assertion about the object. The object may be an audio, image or video. A copy of a digital image is identical to the original. This has in many instances, led to the use of digital content with malicious intent. One way to protect multimedia data against illegal recording and retransmission is to embed a signal, called digital signature or copyright label or watermark that authenticates the owner of the data. Data hiding, schemes to embed secondary data in digital media, have made considerable progress in recent years and attracted attention from both academia and industry. Techniques have been proposed for a variety of applications, including ownership protection, authentication and access control. Imperceptibility, robustness against moderate processing such as compression, and the ability to hide many bits are the basic but rat..
Comparative evaluation of video watermarking techniques in the uncompressed domain
Thesis (MScEng)--Stellenbosch University, 2012.ENGLISH ABSTRACT: Electronic watermarking is a method whereby information can be imperceptibly
embedded into electronic media, while ideally being robust against common signal
manipulations and intentional attacks to remove the embedded watermark. This
study evaluates the characteristics of uncompressed video watermarking techniques
in terms of visual characteristics, computational complexity and robustness against
attacks and signal manipulations.
The foundations of video watermarking are reviewed, followed by a survey of
existing video watermarking techniques. Representative techniques from different
watermarking categories are identified, implemented and evaluated.
Existing image quality metrics are reviewed and extended to improve their performance
when comparing these video watermarking techniques. A new metric for
the evaluation of inter frame flicker in video sequences is then developed.
A technique for possibly improving the robustness of the implemented discrete
Fourier transform technique against rotation is then proposed. It is also shown that
it is possible to reduce the computational complexity of watermarking techniques
without affecting the quality of the original content, through a modified watermark
embedding method.
Possible future studies are then recommended with regards to further improving
watermarking techniques against rotation.AFRIKAANSE OPSOMMING: ’n Elektroniese watermerk is ’n metode waardeur inligting onmerkbaar in elektroniese
media vasgelê kan word, met die doel dat dit bestand is teen algemene manipulasies
en doelbewuste pogings om die watermerk te verwyder. In hierdie navorsing
word die eienskappe van onsaamgeperste video watermerktegnieke ondersoek
in terme van visuele eienskappe, berekeningskompleksiteit en weerstandigheid teen
aanslae en seinmanipulasies.
Die onderbou van video watermerktegnieke word bestudeer, gevolg deur ’n oorsig
van reedsbestaande watermerktegnieke. Verteenwoordigende tegnieke vanuit verskillende
watermerkkategorieë word geïdentifiseer, geïmplementeer en geëvalueer.
Bestaande metodes vir die evaluering van beeldkwaliteite word bestudeer en uitgebrei
om die werkverrigting van die tegnieke te verbeter, spesifiek vir die vergelyking
van watermerktegnieke. ’n Nuwe stelsel vir die evaluering van tussenraampie flikkering
in video’s word ook ontwikkel.
’n Tegniek vir die moontlike verbetering van die geïmplementeerde diskrete Fourier
transform tegniek word voorgestel om die tegniek se bestandheid teen rotasie
te verbeter. Daar word ook aangetoon dat dit moontlik is om die berekeningskompleksiteit
van watermerktegnieke te verminder, sonder om die kwaliteit van die
oorspronklike inhoud te beïnvloed, deur die gebruik van ’n verbeterde watermerkvasleggingsmetode.
Laastens word aanbevelings vir verdere navorsing aangaande die verbetering van
watermerktegnieke teen rotasie gemaak
Protecting the Intellectual Property of Diffusion Models by the Watermark Diffusion Process
Diffusion models have emerged as state-of-the-art deep generative
architectures with the increasing demands for generation tasks. Training large
diffusion models for good performance requires high resource costs, making them
valuable intellectual properties to protect. While most of the existing
ownership solutions, including watermarking, mainly focus on discriminative
models. This paper proposes WDM, a novel watermarking method for diffusion
models, including watermark embedding, extraction, and verification. WDM embeds
the watermark data through training or fine-tuning the diffusion model to learn
a Watermark Diffusion Process (WDP), different from the standard diffusion
process for the task data. The embedded watermark can be extracted by sampling
using the shared reverse noise from the learned WDP without degrading
performance on the original task. We also provide theoretical foundations and
analysis of the proposed method by connecting the WDP to the diffusion process
with a modified Gaussian kernel. Extensive experiments are conducted to
demonstrate its effectiveness and robustness against various attacks
DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models
Recently, Generative Diffusion Models (GDMs) have showcased their remarkable
capabilities in learning and generating images. A large community of GDMs has
naturally emerged, further promoting the diversified applications of GDMs in
various fields. However, this unrestricted proliferation has raised serious
concerns about copyright protection. For example, artists including painters
and photographers are becoming increasingly concerned that GDMs could
effortlessly replicate their unique creative works without authorization. In
response to these challenges, we introduce a novel watermarking scheme,
DiffusionShield, tailored for GDMs. DiffusionShield protects images from
copyright infringement by GDMs through encoding the ownership information into
an imperceptible watermark and injecting it into the images. Its watermark can
be easily learned by GDMs and will be reproduced in their generated images. By
detecting the watermark from generated images, copyright infringement can be
exposed with evidence. Benefiting from the uniformity of the watermarks and the
joint optimization method, DiffusionShield ensures low distortion of the
original image, high watermark detection performance, and the ability to embed
lengthy messages. We conduct rigorous and comprehensive experiments to show the
effectiveness of DiffusionShield in defending against infringement by GDMs and
its superiority over traditional watermarking methods
Augmented watermarking
This thesis provides an augmented watermarking technique wherein noise is based on the watermark added to the watermarked image so that only the end user who has the key for embedding the watermark can both remove the noise and watermark to get a final clear image. The recovery for different values of noise is observed. This system may be implemented as a basic digital rights management system by defining a regime of partial rights using overlaid watermarks, together with respectively added layers of noise, in which the rights of the users define the precision with which the signals may be viewed
Privacy-preserving information hiding and its applications
The phenomenal advances in cloud computing technology have raised concerns about data privacy. Aided by the modern cryptographic techniques such as homomorphic encryption, it has become possible to carry out computations in the encrypted domain and process data without compromising information privacy. In this thesis, we study various classes of privacy-preserving information hiding schemes and their real-world applications for cyber security, cloud computing, Internet of things, etc.
Data breach is recognised as one of the most dreadful cyber security threats in which private data is copied, transmitted, viewed, stolen or used by unauthorised parties. Although encryption can obfuscate private information against unauthorised viewing, it may not stop data from illegitimate exportation. Privacy-preserving Information hiding can serve as a potential solution to this issue in such a manner that a permission code is embedded into the encrypted data and can be detected when transmissions occur.
Digital watermarking is a technique that has been used for a wide range of intriguing applications such as data authentication and ownership identification. However, some of the algorithms are proprietary intellectual properties and thus the availability to the general public is rather limited. A possible solution is to outsource the task of watermarking to an authorised cloud service provider, that has legitimate right to execute the algorithms as well as high computational capacity. Privacypreserving Information hiding is well suited to this scenario since it is operated in the encrypted domain and hence prevents private data from being collected by the cloud.
Internet of things is a promising technology to healthcare industry. A common framework consists of wearable equipments for monitoring the health status of an individual, a local gateway device for aggregating the data, and a cloud server for storing and analysing the data. However, there are risks that an adversary may attempt to eavesdrop the wireless communication, attack the gateway device or even access to the cloud server. Hence, it is desirable to produce and encrypt the data simultaneously and incorporate secret sharing schemes to realise access control. Privacy-preserving secret sharing is a novel research for fulfilling this function.
In summary, this thesis presents novel schemes and algorithms, including:
• two privacy-preserving reversible information hiding schemes based upon symmetric cryptography using arithmetic of quadratic residues and lexicographic permutations, respectively.
• two privacy-preserving reversible information hiding schemes based upon asymmetric cryptography using multiplicative and additive privacy homomorphisms, respectively.
• four predictive models for assisting the removal of distortions inflicted by information hiding based respectively upon projection theorem, image gradient, total variation denoising, and Bayesian inference.
• three privacy-preserving secret sharing algorithms with different levels of generality
Robust feature-based 3D mesh segmentation and visual mask with application to QIM 3D watermarking
The last decade has seen the emergence of 3D meshes in industrial, medical and entertainment applications. Many researches, from both the academic and the industrial sectors, have become aware of their intellectual property protection arising with their increasing use. The context of this master thesis is related to the digital rights management (DRM) issues and more particularly to 3D digital watermarking which is a technical tool that by means of hiding secret information can offer copyright protection, content authentication, content tracking (fingerprinting), steganography (secret communication inside another media), content enrichment etc. Up to now, 3D watermarking non-blind schemes have reached good levels in terms of robustness against a large set of attacks which 3D models can undergo (such as noise addition, decimation, reordering, remeshing, etc.). Unfortunately, so far blind 3D watermarking schemes do not present a good resistance to de-synchronization attacks (such as cropping or resampling). This work focuses on improving the Spread Transform Dither Modulation (STDM) application on 3D watermarking, which is an extension of the Quantization Index Modulation (QIM), through both the use of the perceptual model presented, which presents good robustness against noising and smoothing attacks, and the the application of an algorithm which provides robustness noising and smoothing attacks, and the the application of an algorithm which provides robustness against reordering and cropping attacks based on robust feature detection. Similar to other watermarking techniques, imperceptibility constraint is very important for 3D objects watermarking. For this reason, this thesis also explores the perception of the distortions related to the watermark embed process as well as to the alterations produced by the attacks that a mesh can undergo
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant
signal such as a piece of audio, video, or image. Digital watermarking is a
form of data hiding where identifying data is robustly embedded so that it can
resist tampering and be used to identify the original owners of the media.
Steganography, another form of data hiding, embeds data for the purpose of
secure and secret communication. This survey summarises recent developments in
deep learning techniques for data hiding for the purposes of watermarking and
steganography, categorising them based on model architectures and noise
injection methods. The objective functions, evaluation metrics, and datasets
used for training these data hiding models are comprehensively summarised.
Finally, we propose and discuss possible future directions for research into
deep data hiding techniques
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