EmbAu: A Novel Technique to Embed Audio Data Using Shuffled Frog Leaping Algorithm

• URL: http://arxiv.org/abs/2312.08417v1
• Date: Wed, 13 Dec 2023 17:34:08 GMT
• Title: EmbAu: A Novel Technique to Embed Audio Data Using Shuffled Frog Leaping Algorithm
• Authors: Sahil Nokhwal, Saurabh Pahune, Ankit Chaudhary
• Abstract summary: The aim of steganographic algorithms is to identify the appropriate pixel positions in the host or cover image, where bits of sensitive information can be concealed for data encryption. Work is being done to improve the capacity to integrate sensitive information and to maintain the visual appearance of the steganographic image. We use the Shuffled Frog Leaping Algorithm (SFLA) to determine the order of pixels by which sensitive information can be placed in the cover image.
• Score: 0.7673339435080445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The aim of steganographic algorithms is to identify the appropriate pixel positions in the host or cover image, where bits of sensitive information can be concealed for data encryption. Work is being done to improve the capacity to integrate sensitive information and to maintain the visual appearance of the steganographic image. Consequently, steganography is a challenging research area. In our currently proposed image steganographic technique, we used the Shuffled Frog Leaping Algorithm (SFLA) to determine the order of pixels by which sensitive information can be placed in the cover image. To achieve greater embedding capacity, pixels from the spatial domain of the cover image are carefully chosen and used for placing the sensitive data. Bolstered via image steganography, the final image after embedding is resistant to steganalytic attacks. The SFLA algorithm serves in the optimal pixels selection of any colored (RGB) cover image for secret bit embedding. Using the fitness function, the SFLA benefits by reaching a minimum cost value in an acceptable amount of time. The pixels for embedding are meticulously chosen to minimize the host image's distortion upon embedding. Moreover, an effort has been taken to make the detection of embedded data in the steganographic image a formidable challenge. Due to the enormous need for audio data encryption in the current world, we feel that our suggested method has significant potential in real-world applications. In this paper, we propose and compare our strategy to existing steganographic methods.

Related papers

• Neural Cover Selection for Image Steganography [7.7961128660417325]
  In steganography, selecting an optimal cover image, referred to as cover selection, is pivotal for effective message concealment. Inspired by recent advancements in generative models, we introduce a novel cover selection framework. Our method shows significant advantages in message recovery and image quality.
  arXiv  Detail & Related papers  (2024-10-23T18:32:34Z)
• Enabling Practical and Privacy-Preserving Image Processing [5.526464269029825]
  Homomorphic Encryption (FHE) enables computations on encrypted data, preserving confidentiality without the need for decryption. Traditional FHE methods often encrypt images by monolithic data blocks, instead of pixels. We propose and implement a pixel-level homomorphic encryption approach, iCHEETAH, based on the CKKS scheme.
  arXiv  Detail & Related papers  (2024-09-05T14:22:02Z)
• Learning Camouflaged Object Detection from Noisy Pseudo Label [60.9005578956798]
  This paper introduces the first weakly semi-supervised Camouflaged Object Detection (COD) method. It aims for budget-efficient and high-precision camouflaged object segmentation with an extremely limited number of fully labeled images. We propose a noise correction loss that facilitates the model's learning of correct pixels in the early learning stage. When using only 20% of fully labeled data, our method shows superior performance over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-18T04:53:51Z)
• An Effective Approach to Scramble Multiple Diagnostic Imageries Using Chaos-Based Cryptography [0.0]
  We provide a chaotic system-based medical picture encryption method. The permutation based on plain image and chaotic keys is offered to shuffle the plain picture's pixels to other rows and columns. We analyze the chaotic behavior of the proposed system using various techniques and tests such as bifurcation plots, Lyapunov exponents, MSE, PSNR tests, and histogram analysis.
  arXiv  Detail & Related papers  (2024-05-02T05:18:46Z)
• Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis [65.7968515029306]
  We propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for Pose-Guided Person Image Synthesis (PGPIS) A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt.
  arXiv  Detail & Related papers  (2024-02-28T06:07:07Z)
• Secure Information Embedding in Images with Hybrid Firefly Algorithm [2.9182357325967145]
  This research introduces a novel steganographic approach for concealing a confidential portable document format (PDF) document within a host image. The purpose of this search is to accomplish two main goals: increasing the host image's capacity and reducing distortion. The findings indicate a decrease in image distortion and an accelerated rate of convergence in the search process.
  arXiv  Detail & Related papers  (2023-12-21T01:50:02Z)
• Perceptual Image Compression with Cooperative Cross-Modal Side Information [53.356714177243745]
  We propose a novel deep image compression method with text-guided side information to achieve a better rate-perception-distortion tradeoff. Specifically, we employ the CLIP text encoder and an effective Semantic-Spatial Aware block to fuse the text and image features.
  arXiv  Detail & Related papers  (2023-11-23T08:31:11Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• Hiding Data in Colors: Secure and Lossless Deep Image Steganography via Conditional Invertible Neural Networks [20.81947232336795]
  Existing deep image steganography methods only consider the visual similarity of container images to host images, and neglect the statistical security (stealthiness) of container images. We propose deep image steganography that can embed data with arbitrary types into images for secure data hiding and lossless data revealing.
  arXiv  Detail & Related papers  (2022-01-19T07:09:36Z)
• Robust Data Hiding Using Inverse Gradient Attention [82.73143630466629]
  In the data hiding task, each pixel of cover images should be treated differently since they have divergent tolerabilities. We propose a novel deep data hiding scheme with Inverse Gradient Attention (IGA), combing the ideas of adversarial learning and attention mechanism. Empirically, extensive experiments show that the proposed model outperforms the state-of-the-art methods on two prevalent datasets.
  arXiv  Detail & Related papers  (2020-11-21T19:08:23Z)
• Free-Form Image Inpainting via Contrastive Attention Network [64.05544199212831]
  In image inpainting tasks, masks with any shapes can appear anywhere in images which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. We propose a self-supervised Siamese inference network to improve the robustness and generalization.
  arXiv  Detail & Related papers  (2020-10-29T14:46:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.