862 research outputs found
Emerging Applications of Reversible Data Hiding
Reversible data hiding (RDH) is one special type of information hiding, by
which the host sequence as well as the embedded data can be both restored from
the marked sequence without loss. Beside media annotation and integrity
authentication, recently some scholars begin to apply RDH in many other fields
innovatively. In this paper, we summarize these emerging applications,
including steganography, adversarial example, visual transformation, image
processing, and give out the general frameworks to make these operations
reversible. As far as we are concerned, this is the first paper to summarize
the extended applications of RDH.Comment: ICIGP 201
Normalized Weighting Schemes for Image Interpolation Algorithms
This paper presents and evaluates four weighting schemes for image
interpolation algorithms. The first scheme is based on the normalized area of
the circle, whose diameter is equal to the minimum side of a tetragon. The
second scheme is based on the normalized area of the circle, whose radius is
equal to the hypotenuse. The third scheme is based on the normalized area of
the triangle, whose base and height are equal to the hypotenuse and virtual
pixel length, respectively. The fourth weighting scheme is based on the
normalized area of the circle, whose radius is equal to the virtual pixel
length-based hypotenuse. Experiments demonstrated debatable algorithm
performances and the need for further research.Comment: 8 pages, 14 figure
Hybrid chaotic map with L-shaped fractal Tromino for image encryption and decryption
Insecure communication in digital image security and image storing are considered as important challenges. Moreover, the existing approaches face problems related to improper security at the time of image encryption and decryption. In this research work, a wavelet environment is obtained by transforming the cover image utilizing integer wavelet transform (IWT) and hybrid discrete cosine transform (DCT) to completely prevent false errors. Then the proposed hybrid chaotic map with L-shaped fractal Tromino offers better security to maintain image secrecy by means of encryption and decryption. The proposed work uses fractal encryption with the combination of L-shaped Tromino theorem for enhancement of information hiding. The regions of L-shaped fractal Tromino are sensitive to variations, thus are embedded in the watermark based on a visual watermarking technique known as reversible watermarking. The experimental results showed that the proposed method obtained peak signal-to-noise ratio (PSNR) value of 56.82dB which is comparatively higher than the existing methods that are, Beddington, free, and Lawton (BFL) map with PSNR value of 8.10 dB, permutation substitution, and Boolean operation with PSNR value of 21.19 dB and deoxyribonucleic acid (DNA) level permutation-based logistic map with PSNR value of 21.27 dB
Low-frequency Image Deep Steganography: Manipulate the Frequency Distribution to Hide Secrets with Tenacious Robustness
Image deep steganography (IDS) is a technique that utilizes deep learning to
embed a secret image invisibly into a cover image to generate a container
image. However, the container images generated by convolutional neural networks
(CNNs) are vulnerable to attacks that distort their high-frequency components.
To address this problem, we propose a novel method called Low-frequency Image
Deep Steganography (LIDS) that allows frequency distribution manipulation in
the embedding process. LIDS extracts a feature map from the secret image and
adds it to the cover image to yield the container image. The container image is
not directly output by the CNNs, and thus, it does not contain high-frequency
artifacts. The extracted feature map is regulated by a frequency loss to ensure
that its frequency distribution mainly concentrates on the low-frequency
domain. To further enhance robustness, an attack layer is inserted to damage
the container image. The retrieval network then retrieves a recovered secret
image from a damaged container image. Our experiments demonstrate that LIDS
outperforms state-of-the-art methods in terms of robustness, while maintaining
high fidelity and specificity. By avoiding high-frequency artifacts and
manipulating the frequency distribution of the embedded feature map, LIDS
achieves improved robustness against attacks that distort the high-frequency
components of container images
Real-time image dehazing by superpixels segmentation and guidance filter
Haze and fog had a great influence on the quality of images, and to eliminate this, dehazing and defogging are applied. For this purpose, an effective and automatic dehazing method is proposed. To dehaze a hazy image, we need to estimate two important parameters such as atmospheric light and transmission map. For atmospheric light estimation, the superpixels segmentation method is used to segment the input image. Then each superpixel intensities are summed and further compared with each superpixel individually to extract the maximum intense superpixel. Extracting the maximum intense superpixel from the outdoor hazy image automatically selects the hazy region (atmospheric light). Thus, we considered the individual channel intensities of the extracted maximum intense superpixel as an atmospheric light for our proposed algorithm. Secondly, on the basis of measured atmospheric light, an initial transmission map is estimated. The transmission map is further refined through a rolling guidance filter that preserves much of the image information such as textures, structures and edges in the final dehazed output. Finally, the haze-free image is produced by integrating the atmospheric light and refined transmission with the haze imaging model. Through detailed experimentation on several publicly available datasets, we showed that the proposed model achieved higher accuracy and can restore high-quality dehazed images as compared to the state-of-the-art models. The proposed model could be deployed as a real-time application for real-time image processing, real-time remote sensing images, real-time underwater images enhancement, video-guided transportation, outdoor surveillance, and auto-driver backed systems
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
We propose a novel multi-stage depth super-resolution network, which
progressively reconstructs high-resolution depth maps from explicit and
implicit high-frequency features. The former are extracted by an efficient
transformer processing both local and global contexts, while the latter are
obtained by projecting color images into the frequency domain. Both are
combined together with depth features by means of a fusion strategy within a
multi-stage and multi-scale framework. Experiments on the main benchmarks, such
as NYUv2, Middlebury, DIML and RGBDD, show that our approach outperforms
existing methods by a large margin (~20% on NYUv2 and DIML against the
contemporary work DADA, with 16x upsampling), establishing a new
state-of-the-art in the guided depth super-resolution task
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
- …