Measuring hyperplasia in Atlantic salmon gills can give important insight into fish health and environmental conditions such as water quality. This paper proposes a novel histology image classification technique to identify hyperplastic regions using an emerging signal decomposition technique, Empirical Wavelet Transform (EWT) in combination with a fully connected neural network (FCNN). Due to its adaptive nature, we hypothesise and show that EWT effectively represents unique features of gill histopathology whole slide images that help in the classification task. Our hybrid approach is unique and significantly outperformed regular deep learning-based methods considering a joint speed-accuracy metric.
Pontederia crassipes, commonly known as water hyacinth (WH), is a highly invasive aquatic weed and caused significant ecological and economic impact across the world. Remediation action includes manual monitoring and removal which are often time consuming and expensive. This paper proposes the use of multi-temporal multi-spectral drone imagery for WH mapping and monitoring in Patancheru Lake, Hyderabad, India. The data collection was done in two steps: 1) multi-spectral drone imagery and 2) ground optical image capturing through an Android mobile application. Data was collected in regular interval starting from January 2021. Spectral bands were used to produce the WH detection and mapping. We compare spectral signature of clean and infested water for five different sites inside the lake. Multitemporal water quality samples of these sites were also collected together with drone data to analyse the effect of WH infestation on those parameters. The multispectral data was processed using an unsupervised machine learning classifier named expectation maximisation (EM) clustering to create a segmentation map indicating WH, water and other regions.
Advances in deep neural networks (DNN) and distributed ledger technology (DLT) have shown major influence on media security, authenticity and privacy. Current deepfake techniques can produce near realistic media content which can be used in both good and bad intended use cases. At the same time, DLTs are finding their way in the industry as fair, transparent and reliable means for content distribution. In particular non-fungible tokens (NFTs) are emerging in the digital art market. However, such new developments also introduce new challenges, including the need for robust and reliable metadata, a mechanism to secure the media and associated metadata, means to verify authenticity and interoperability between various stakeholders. This paper identifies emerging challenges in fake media and NFT, and proposes a novel framework to effectively cope with secure media applications allowing for a structured, systematic, and interoperable solution. The framework relies on an architecture that is modular, flexible, extensible, and scalable in the sense that it can be implemented in both lighter as well as more feature-rich and more complex configurations depending on the underlying application, needed features and available resources, while enabling products and services in various ecosystems with desired trust and security capabilities. The framework is inspired by activities and developments within JPEG standardisation related to security, authenticity and privacy.
Media assets can easily be manipulated with photo editing software or artificially created using deep learning techniques. This can be done with the intention to mislead, but also for creative or educational purposes. Clear annotation of media modifications is a crucial element to assess trustworthiness. However, these annotations should be attached securly to prevent them from being compromised. In addition, to achieve a wide adoption, interoperability is essential. This paper gives an overview of the media manipulation history, discusses the state-of-the-art and challenges related to AI-based detection methods. The paper then introduces JPEG Fake Media as a provenance-based sustainable approach to secure and trustworthy media annotation. JPEG Fake Media has the objective to produce a standard that can facilitate secure and reliable annotation of media asset creation and modifications. The standard shall support good faith usage scenarios as well as those with malicious intent.
Privacy and security, copyright violations and fake news are emerging challenges in digital media. Social media and data leaks increase risk of user privacy. Creative media particularly images are often susceptible to copyright violations which poses a serious problem to the industry. On the other hand, doctored images using photo editing tools and computer generated images may give a false impression of reality and add to the problem of fake news. These problems demand solutions to protect images and associated metadata as well as methods that can proof the integrity of digital media. For these reasons, the JPEG standardization committee has been working on a new Privacy and Security standard that provides solutions to support privacy and security focused workflows. The standard defines tools to support protection and integrity across the wide range of JPEG image standards. Related to image integrity, blockchain technology provides a solution for creating tamper proof distributed ledgers. However, adopting blockchain technology for digital image integrity poses several challenges at the technology level as well as at the level of privacy legislation. In addition, if blockchain technology is adopted to support media applications, it needs to be closely integrated with a widely adopted standard to ensure broad interoperability. Therefore, the JPEG committee initiated an activity to explore standardization needs related to media blockchain and distributed ledger technologies (DLT). This paper explains the scope and implementation of the JPEG Privacy and Security standard and presents the current state of the exploration on standardization needs related to media blockchain applications.
KEYWORDS: Digital watermarking, Video, Visualization, Motion models, Visual process modeling, Video compression, Wavelets, Cameras, Data modeling, Performance modeling
Imperceptibility and robustness are two key but complementary requirements of any watermarking algorithm. Low-strength watermarking yields high imperceptibility but exhibits poor robustness. High-strength watermarking schemes achieve good robustness but often suffer from embedding distortions resulting in poor visual quality in host media. This paper proposes a unique video watermarking algorithm that offers a fine balance between imperceptibility and robustness using motion compensated wavelet-based visual attention model (VAM). The proposed VAM includes spatial cues for visual saliency as well as temporal cues. The spatial modeling uses the spatial wavelet coefficients while the temporal modeling accounts for both local and global motion to arrive at the spatiotemporal VAM for video. The model is then used to develop a video watermarking algorithm, where a two-level watermarking weighting parameter map is generated from the VAM saliency maps using the saliency model and data are embedded into the host image according to the visual attentiveness of each region. By avoiding higher strength watermarking in the visually attentive region, the resulting watermarked video achieves high perceived visual quality while preserving high robustness. The proposed VAM outperforms the state-of-the-art video visual attention methods in joint saliency detection and low computational complexity performance. For the same embedding distortion, the proposed visual attention-based watermarking achieves up to 39% (nonblind) and 22% (blind) improvement in robustness against H.264/AVC compression, compared to existing watermarking methodology that does not use the VAM. The proposed visual attention-based video watermarking results in visual quality similar to that of low-strength watermarking and a robustness similar to those of high-strength watermarking.
In this paper a universal embedding distortion model for wavelet based watermarking is presented. The present
work extends our previous work on modelling embedding distortion for watermarking algorithms that use orthonormal
wavelet kernels to non-orthonormal wavelet kernels, such as biorthogonal wavelets. By using a common
framework for major wavelet based watermarking algorithms and the Parseval's energy conservation theorem
for orthonormal transforms, we propose that the distortion performance, measured using the mean square error
(MSE), is proportional to the sum of energy of wavelet coefficients to be modified by watermark embedding. The
extension of the model to non-orthonormal wavelet kernel is obtained by rescaling the sum of energy of wavelet
coefficients to be modified by watermark embedding using a weighting parameter that follows the energy conservation
theorems in wavelet frames. The proposed model is useful to find optimum input parameters, such as,
the wavelet kernel, coefficient selections and subband choices, for a given wavelet based watermarking algorithm.
A framework for evaluating wavelet based watermarking schemes against scalable coded visual media content
adaptation attacks is presented. The framework, Watermark Evaluation Bench for Content Adaptation Modes
(WEBCAM), aims to facilitate controlled evaluation of wavelet based watermarking schemes under MPEG-21
part-7 digital item adaptations (DIA). WEBCAM accommodates all major wavelet based watermarking in single
generalised framework by considering a global parameter space, from which the optimum parameters for a specific
algorithm may be chosen. WEBCAM considers the traversing of media content along various links and required
content adaptations at various nodes of media supply chains. In this paper, the content adaptation is emulated
by the JPEG2000 coded bit stream extraction for various spatial resolution and quality levels of the content.
The proposed framework is beneficial not only as an evaluation tool but also as design tool for new wavelet based
watermark algorithms by picking and mixing of available tools and finding the optimum design parameters.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.