505 research outputs found

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Encryption and Decryption of Images with Pixel Data Modification Using Hand Gesture Passcodes

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    To ensure data security and safeguard sensitive information in society, image encryption and decryption as well as pixel data modifications, are essential. To avoid misuse and preserve trust in our digital environment, it is crucial to use these technologies responsibly and ethically. So, to overcome some of the issues, the authors designed a way to modify pixel data that would hold the hidden information. The objective of this work is to change the pixel values in a way that can be used to store information about black and white image pixel data. Prior to encryption and decryption, by using Python we were able to construct a passcode with hand gestures in the air, then encrypt it without any data loss. It concentrates on keeping track of simply two pixel values. Thus, pixel values are slightly changed to ensure the masked image is not misleading. Considering that the RGB values are at their border values of 254, 255 the test cases of masking overcome issues with the corner values susceptibility

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Reversible Image Watermarking Using Modified Quadratic Difference Expansion and Hybrid Optimization Technique

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    With increasing copyright violation cases, watermarking of digital images is a very popular solution for securing online media content. Since some sensitive applications require image recovery after watermark extraction, reversible watermarking is widely preferred. This article introduces a Modified Quadratic Difference Expansion (MQDE) and fractal encryption-based reversible watermarking for securing the copyrights of images. First, fractal encryption is applied to watermarks using Tromino's L-shaped theorem to improve security. In addition, Cuckoo Search-Grey Wolf Optimization (CSGWO) is enforced on the cover image to optimize block allocation for inserting an encrypted watermark such that it greatly increases its invisibility. While the developed MQDE technique helps to improve coverage and visual quality, the novel data-driven distortion control unit ensures optimal performance. The suggested approach provides the highest level of protection when retrieving the secret image and original cover image without losing the essential information, apart from improving transparency and capacity without much tradeoff. The simulation results of this approach are superior to existing methods in terms of embedding capacity. With an average PSNR of 67 dB, the method shows good imperceptibility in comparison to other schemes

    An image steganography using improved hyper-chaotic Henon map and fractal Tromino

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    Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively

    Combinatorial Low-Binding Affinity Polymersomes for Targeting Dendritic Cells: Towards Cancer Vaccine Development

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    Conventional DNA cancer vaccines fail to adequately stimulate the adaptive immune system and to establish proper immunisation. This is reflected in clinical practice, where only a handful of them have been approved by the FDA. Within this project the use of the pH-sensitive polymer Poly (2-(methacryloyloxyethyl phosphorylcholine)-poly(2-(diisopropylamino-ethyl methacrylate) (PMPC-PDPA) has been investigated for DNA antigen encapsulation and intracellular delivery. By implementing a dendritic cellular model (DC2.4), it was demonstrated the expression and surface presentation of the antigen model (SIINFEKL). Furthermore, exploratory experiments highlighted the inflammatory power of the immunomodulator cyclic guanosine monophosphate–adenosine monophosphate (cGAMP) in in vitro settings, with potential implications for in vivo cancer vaccines. Moreover, current strategies of design for active targeting nanoparticles (NPs) are suboptimal and characterised by off-target binding and side effects. In this work, it was demonstrated a paradigm shift in the design of active targeting nanoparticles based on the concepts of the ‘range selectivity’ theory. Specific Ligands for the phenotypic targeting of dendritic cells (DCs) were selected (PMPC, mUNO and PEP4) and conjugated to the respective polymer, such as PMPC-PDPA or polyethylene glycol-poly (lactic acid) (PEG- PLA). Multivalent and multiplexing POs were prepared and tested in vitro, proving experimentally the validity of computational hypotheses. Multivalent and combinatorial POs were also intradermally injected into animal models to further corroborate in vitro experimental evidence. It was envisioned that the implementation of empirical observation combined with in silico simulation would help to define the optimal range of the number of ligands on a vesicle for the phenotypic targeting of DCs, ultimately improving the intracellular co-delivery of antigen and adjuvant for the development of a cancer vaccine

    STaSy: Score-based Tabular data Synthesis

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    Tabular data synthesis is a long-standing research topic in machine learning. Many different methods have been proposed over the past decades, ranging from statistical methods to deep generative methods. However, it has not always been successful due to the complicated nature of real-world tabular data. In this paper, we present a new model named Score-based Tabular data Synthesis (STaSy) and its training strategy based on the paradigm of score-based generative modeling. Despite the fact that score-based generative models have resolved many issues in generative models, there still exists room for improvement in tabular data synthesis. Our proposed training strategy includes a self-paced learning technique and a fine-tuning strategy, which further increases the sampling quality and diversity by stabilizing the denoising score matching training. Furthermore, we also conduct rigorous experimental studies in terms of the generative task trilemma: sampling quality, diversity, and time. In our experiments with 15 benchmark tabular datasets and 7 baselines, our method outperforms existing methods in terms of task-dependant evaluations and diversity. Code is available at https://github.com/JayoungKim408/STaSy.Comment: 27 pages, Accepted by ICLR 2023 for spotlight presentation, Official code: https://github.com/JayoungKim408/STaS

    Changing Priorities. 3rd VIBRArch

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    In order to warrant a good present and future for people around the planet and to safe the care of the planet itself, research in architecture has to release all its potential. Therefore, the aims of the 3rd Valencia International Biennial of Research in Architecture are: - To focus on the most relevant needs of humanity and the planet and what architectural research can do for solving them. - To assess the evolution of architectural research in traditionally matters of interest and the current state of these popular and widespread topics. - To deepen in the current state and findings of architectural research on subjects akin to post-capitalism and frequently related to equal opportunities and the universal right to personal development and happiness. - To showcase all kinds of research related to the new and holistic concept of sustainability and to climate emergency. - To place in the spotlight those ongoing works or available proposals developed by architectural researchers in order to combat the effects of the COVID-19 pandemic. - To underline the capacity of architectural research to develop resiliency and abilities to adapt itself to changing priorities. - To highlight architecture's multidisciplinarity as a melting pot of multiple approaches, points of view and expertise. - To open new perspectives for architectural research by promoting the development of multidisciplinary and inter-university networks and research groups. For all that, the 3rd Valencia International Biennial of Research in Architecture is open not only to architects, but also for any academic, practitioner, professional or student with a determination to develop research in architecture or neighboring fields.Cabrera Fausto, I. (2023). Changing Priorities. 3rd VIBRArch. Editorial Universitat Politècnica de València. https://doi.org/10.4995/VIBRArch2022.2022.1686
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