Invisible Image Research Articles

Invisible Images: Pictures, Illustrations and Visual Data in the Text-Centric System of the Library

This study investigates how the library information infrastructure steers not only the access, searchability and retrievability of the images it preserves but also, on a more profound level, how images are understood as sources of information. Using the National Library of Sweden as a case study it first outlines how images are handled and conceptualized – as single pictures, as illustrations secondary to text, or as visual data, repositories of iconographic visual content. Second, through close analysis of five types of images common in library collections in general, it examines how they are classified, demarcated and made accessible. As this study shows, both analogue and digital information infrastructures in the library are, on a very profound level, tailored for text. Such unuttered, even unconscious, text-centrism has the effect that images as sources of information are made literally and figuratively invisible.

Molecular Reconfiguration of Disordered Tellurium Oxide Transistors with Biomimetic Spectral Selectivity.

Reconfigurable devices with field-effect transistor features and neuromorphic behaviors are promising for enhancing data processing capability and reducing power consumption in next-generation semiconductor platforms. However, commonly used 2D materials for reconfigurable devices require additional modulation terminals and suffer from complex and stringent operating rules to obtain specific functionalities. Here, a p-type disordered tellurium oxide is introduced that realizes dual-mode reconfigurability as a logic transistor and a neuromorphic device. Due to the disordered film surface, the enhanced adsorption of oxygen molecules and laser-induced desorption concurrently regulate the carrier concentration in the channel. The device exhibits high-performance p-type characteristics with a field-effect hole mobility of 10.02 cm2 V-1 s-1 and an Ion/Ioff ratio exceeding 106 in the transistor mode. As a neuromorphic device, the vision system exhibits biomimetic bee vision, explicitly responding to the blue-to-ultraviolet light. Finally, in-sensor denoising and invisible image recognition in static and dynamic scenarios are achieved.

Social Media Authentication and Combating Deepfakes using Semi-fragile Invisible Image Watermarking

With the significant advances in deep generative models for image and video synthesis, Deepfakes and manipulated media have raised severe societal concerns. Conventional machine learning classifiers for deepfake detection often fail to cope with evolving deepfake generation technology and are susceptible to adversarial attacks. Alternatively, invisible image watermarking is being researched as a proactive defense technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. A handful of invisible image watermarking techniques introduced for media authentication have proven vulnerable to basic image processing operations and watermark removal attacks. In response, we have proposed a semi-fragile image watermarking technique that embeds an invisible secret message into real images for media authentication. Our proposed watermarking framework is designed to be fragile to facial manipulations or tampering while being robust to benign image-processing operations and watermark removal attacks. This is facilitated through a unique architecture of our proposed technique consisting of critic and adversarial networks that enforce high image quality and resiliency to watermark removal efforts, respectively, along with the backbone encoder-decoder and the discriminator networks. This allows images shared over the Internet to retain the verifiable watermark as long as facial manipulations or any other Deepfake modification technique is not applied. Thorough experimental investigations on SOTA facial Deepfake datasets demonstrate that our proposed model can embed a \(64\) -bit secret as an imperceptible image watermark that can be recovered with a high-bit recovery accuracy when benign image processing operations are applied while being non-recoverable when unseen Deepfake manipulations are applied. In addition, our proposed watermarking technique demonstrates high resilience to several white-box and black-box watermark removal attacks. Thus, obtaining state-of-the-art performance.

Bone metastasis prediction in non-small-cell lung cancer: primary CT-based radiomics signature and clinical feature

BackgroundRadiomics provided opportunities to quantify the tumor phenotype non-invasively. This study extracted contrast-enhanced computed tomography (CECT) radiomic signatures and evaluated clinical features of bone metastasis in non-small-cell lung cancer (NSCLC). With the combination of the revealed radiomics and clinical features, the predictive modeling on bone metastasis in NSCLC was established.MethodsA total of 318 patients with NSCLC at the Tianjin Medical University Cancer Institute & Hospital was enrolled between January 2009 and December 2019, which included a feature-learning cohort (n = 223) and a validation cohort (n = 95). We trained a radiomics model in 318 CECT images from feature-learning cohort to extract the radiomics features of bone metastasis in NSCLC. The Kruskal-Wallis and the least absolute shrinkage and selection operator regression (LASSO) were used to select bone metastasis-related features and construct the CT radiomics score (Rad-score). Multivariate logistic regression was performed with the combination of the Rad-score and clinical data. A predictive nomogram was subsequently developed.ResultsRadiomics models using CECT scans were significant on bone metastasis prediction in NSCLC. Model performance was enhanced with each information into the model. The radiomics nomogram achieved an AUC of 0.745 (95% confidence interval [CI]: 0.68,0.80) on predicting bone metastasis in the training set and an AUC of 0.808(95% confidence interval [CI]: 0.71,0.88) in the validation set.ConclusionThe revealed invisible image features were of significance on guiding bone metastasis prediction in NSCLC. Based on the combination of the image features and clinical characteristics, the predictive nomogram was established. Such nomogram can be used for the auxiliary screening of bone metastasis in NSCLC.

Brightness Aware Pixel Stretching for Perceptually Invisible Images Using Wavelet Approximation Balancing

Brightness Aware Pixel Stretching for Perceptually Invisible Images Using Wavelet Approximation Balancing

RAWIW: RAW Image Watermarking robust to ISP pipeline

Invisible image watermarking is essential for image copyright protection. Compared to RGB images, RAW format images use a higher dynamic range to capture the radiometric characteristics of the camera sensor, providing greater flexibility in post-processing and retouching. RAW images are considered the original format for distribution and image production, thus requiring copyright protection. Existing watermarking methods typically target RGB images, leaving a gap for RAW images. To address this issue, we propose the first deep learning-based RAWImage Watermarking (RAWIW) framework for copyright protection. Unlike RGB image watermarking, our method achieves cross-domain copyright protection. We directly embed copyright information into RAW images, which can be later extracted from the corresponding RGB images generated by different post-processing methods. To achieve end-to-end training of the framework, we integrate a neural network that simulates the Image Signal Processing (ISP) pipeline to handle the RAW-to-RGB conversion process. To further validate the generalization of our framework to traditional ISP pipelines and its robustness to transmission distortion, we adopt a distortion network. This network simulates noises introduced during the traditional ISP pipeline and transmission. Our extensive experiments demonstrate that RAWIW successfully achieves cross-domain copyright protection for RAW images while maintaining their visual quality and robustness to ISP pipeline distortions.

Exploring the intersection of art, technology, and social issues on Paglen’s work

Media Artivism refers to the use of digital and technological media to raise awareness and facilitate social and political transformation. Paglen’s artistic endeavors represent a powerful activist dimension epitomized in particular in his work Invisible Images (Your Pictures Are Looking at You). In this work, Paglen demonstrates how technology corporations exploit the images of viewers. Consequently, his work urges people to better understand how privacy works in the contemporary digital landscape, thus demanding more transparency and accountability from these corporations. The work of Trevor Paglen serves as an excellent example of the interplay between art, science and technology, underscoring the significance of Media Artivism as a means of raising awareness and promoting social transformation. Paglen’s work offers a model for artists to employ digital and technological tools to engage with pressing political and social concerns and also to interrogate the role of technology in contemporary society. His artistic practise underscores the potential of media artivism as a conduit for critically examining the relationship between art, science and technology, and its potential to effect change in society.

Image Watermarking Using Discrete Wavelet Transform and Singular Value Decomposition for Enhanced Imperceptibility and Robustness

Digital multimedia elements such as text, image, audio, and video can be easily manipulated because of the rapid rise of multimedia technology, making data protection a prime concern. Hence, copyright protection, content authentication, and integrity verification are today’s new challenging issues. To address these issues, digital image watermarking techniques have been proposed by several researchers. Image watermarking can be conducted through several transformations, such as discrete wavelet transform (DWT), singular value decomposition (SVD), orthogonal matrix Q and upper triangular matrix R (QR) decomposition, and non-subsampled contourlet transform (NSCT). However, a single transformation cannot simultaneously satisfy all the design requirements of image watermarking, which makes a platform to design a hybrid invisible image watermarking technique in this work. The proposed work combines four-level (4L) DWT and two-level (2L) SVD. The Arnold map initially encrypts the watermark image, and 2L SVD is applied to it to extract the s components of the watermark image. A 4L DWT is applied to the host image to extract the LL sub-band, and then 2L SVD is applied to extract s components that are embedded into the host image to generate the watermarked image. The dynamic-sized watermark maintains a balanced visual impact and non-blind watermarking preserves the quality and integrity of the host image. We have evaluated the performance after applying several intentional and unintentional attacks and found high imperceptibility and improved robustness with enhanced security to the system than existing state-of-the-art methods.

Kristus (dan) Debata Mulajadi na Bolon: Perjumpaan yang “serupa tapi ‘tak sama” atau “adalah?”

Christ and Debata Mulajadi na Bolon are two distinct entities, understood respectively by Christianity and by Batak-Toba culture. Christ is known as the incarnate logos (John 1:1-5) and the firstborn and invisible image of God (Colossians 1:15), while Debata Mulajadi na Bolon is only approached as “the supreme god of Batak-Toba”; although the titles ascribed to him are similar to those ascribed to God. This article will build on the idea that Christ is Debata Mulajadi na Bolon himself, although it must be acknowledged that there are differences, especially in their Trinity theology. Debata Mulajadi na Bolon is not a pagan figure but God who presents Himself as Debata Mulajadi na Bolon. The three biblical texts that will be discussed in this article are the bridge of this transformation effort, also using two approaches namely Niebuhr’s typology, Christ the Transformer of Culture and Bevans’ synthesized contextual theology model.

Computationally Efficient Scaled Clustering for Perceptually Invisible Image Intensity Tuning

Quality and information content of an image is a vital aspect affecting the performance of any artificial intelligence-related task. Improving the quality of an image while maintaining its color and naturalness in a very small time frame is a challenge. In this article, a computationally efficient scaled clustering-based contrast enhancement method is proposed for enhancing perceptually invisible images and preserving color and naturalness. Pixels in the image are grouped in clusters using the fuzzy c-means algorithm and are assigned membership to different clusters. Membership values of the pixels are modified and scaled up using the cluster centers. Based on these modified and scaled cluster memberships, the intensity level of the pixels is modified. The membership-based proportional modification of the intensity values in the scaled domain improves the color and information content of an image. It is scaled down to the original range of intensity levels, using linear scaling to obtain an enhanced image with better contrast and color information. The generated image is free from artifacts and is natural in appearance. The proposed method is simulated and tested on frequently used standard datasets. The simulation results and the average computation time of the proposed method are compared to some of the well-established conventional and latest techniques.

Semi-Transparent, Pixel-Free Upconversion Goggles with Dual Audio-Visual Communication.

The intractable brittleness and opacity of the crystalline semiconductor restrict the prospect of developing low-cost imaging systems. Here, infrared visualization technologies are established with large-area, semi-transparent organic upconversion devices that bring high-resolution invisible images into sight without photolithography. To exploit all photoinduced charge carriers, a monolithic device structure is proposed built on the infrared-selective, single-component charge generation layer of chloroaluminum phthalocyanine (ClAlPc) coupled to two visible light-emitting layers manipulated with unipolar charges. Transient pump-probe spectroscopy reveals that the ClAlPc-based device exhibits an efficient charge dissociation process under forward bias. This process is indicated by the prompt and strong features of electroabsorption screening. Furthermore, by imposing the electric field, the ultrafast excited state dynamic suggests a prolonged charge carrier lifetime from the ClAlPc, which facilitates the charge utilization for upconversion luminance. For the first time, >30% of the infrared photons are utilized without photomultiplication strategies owing to the trivial spectrum overlap between ClAlPc and the emitter. In addition, the device can broadcast the acoustic signal by synchronizing the device frequency with the light source, which enables to operate it in dual audio-visual mode. The work demonstrates the potential of upconversion devices for affordable infrared imaging in wearable electronics.

Early Diabetic Retinopathy Detection using Deep Learning

Abstract: Typically, hazy and low contrast, Diabetic Retinopathy (DR) results from damage to the blood vessels inside the delicate tissue behind the eye (that is, the retina). Fundus images produced by fundus cameras are frequently imperfect. The risk of blindness in DR patients can be decreased with early detection and treatment. Finding and documenting diabetic retinopathy in people is a challenging and error-prone procedure because it is challenging to capture photographs with colour fundus photography. DR classes can be identified and categorised early by utilising machine learning techniques and a variety of screening criteria. These methods' accuracy, though, is below average. This shortcoming of these techniques can be remedied by using a different technique for the task such as deep learning. This technique uses image metadata to train a deep learning model and learn features using hundreds of classes in DR. This allows experts to create models that can classify invisible images into appropriate classes or levels of acceptable accuracy. This paper proposed a DenseNet201 model to classify fundus images into the correct severity class. We trained the model on 1914 images per class and used checkpoints to save the best weight automatically.

Hybrid and Invisible Digital Image Watermarking Technique Using IWT-DCT and Hopfield Neural Network

According to the characteristic of HVS (Human Visual System) and the association memory ability of the neural network, an adaptive image watermarking algorithm based on a neural network is proposed invisible image watermarking is a secret embedding scheme for hiding secret images into a cover image file and the purpose of invisible watermarking is copyrights protection. Wavelet transformation-based image watermarking techniques provide better robustness for statistical attacks in comparison to Discrete Cosine Transform domain-based image watermarking. The joined method of IWT (Integer Wavelet Transform) and DCT (Discrete Cosine Transform) gives benefits to the two procedures. The IWT has an impediment of portion misfortune in embedding which increments the mean square estimate as SIM and results in diminishing PSNR. The capacity of drawing in is improved by pretreatment and re-treatment of image scrambling and Hopfield neural network. The proposed algorithm presents a hybrid integer wavelet transform and discrete cosine transform-based watermarking technique to obtain increased imperceptibility and robustness compared to the IWT-DCT-based watermarking technique. The proposed watermarking technique reduces the fractional loss compared to DWT-based watermarking.

The progress of radiomics in thyroid nodules.

Due to the development of Artificial Intelligence (AI), Machine Learning (ML), and the improvement of medical imaging equipment, radiomics has become a popular research in recent years. Radiomics can obtain various quantitative features from medical images, highlighting the invisible image traits and significantly enhancing the ability of medical imaging identification and prediction. The literature indicates that radiomics has a high potential in identifying and predicting thyroid nodules. So in this article, we explain the development, definition, and workflow of radiomics. And then, we summarize the applications of various imaging techniques in identifying benign and malignant thyroid nodules, predicting invasiveness and metastasis of thyroid lymph nodes, forecasting the prognosis of thyroid malignancies, and some new advances in molecular level and deep learning. The shortcomings of this technique are also summarized, and future development prospects are provided.

Christ, Image of the Father. On the Lasting Importance of the Christology of Thomas Aquinas

AbstractThis contribution discusses the expression ‘Image of the Father’ as a case in point to Aquinas's approach of naming Christ: Christology and Trinitarian theology, as well as the discussion of analogical naming in divinis, need to be taken together. ‘Image’ and ‘Father’ are predicated differently of Christ and God the Father than of human beings. Moreover, God is ‘Father’ in a different way towards the Son than regarding human beings. Christ is the unique image of the Father: the invisible image of the invisible Father.

Unsupervised Enhancement and Web Tool for Perceptually Invisible Type Degraded Image

Severe contrast degradation in an image result in a substantial loss of crucial visible information. A novel unsupervised image enhancement algorithm is proposed in this paper to unravel hidden visual details in perceptually invisible images. In this paper, new algorithms are designed to solve the range localization problem. The proposed algorithm intends to expand the image range to maximize its intensity level utilization. Outliers present in an image intensity range are truncated based on their power distribution. Based on the type of input image, maxima-minima, or frequency-based expansion of truncated levels, is exercised to avoid over-enhancement. Two novel parameters, namely the transmission map and zero-frequency spread, are coined. The visually enhanced result successfully exhibits the camouflaged information and avoids over-enhancement. Consumer gadgets, such as cameras, have difficulty taking pictures in very dark and cloudy settings. The suggested algorithm can improve low-contrast photos that are both very dark and severely attenuated. The unsupervised nature of the proposed algorithm makes it suitable for application in the field of consumer electronics like mobile phones and digital cameras. A consumer-focused, platform-independent app is created and made available to the general public for additional study.

Artificial bee Colony optimized image enhancement framework for invisible images

Artificial bee Colony optimized image enhancement framework for invisible images

Fuzzified Contrast Enhancement for Nearly Invisible Images

Image enhancement is a basic requirement for any computer vision application for further processing of an image. A common limitation with most of the existing methods, when applied to nearly invisible images, is the loss of color details during the enhancement process. So, a fuzzy c-means clustering-based method for image enhancement is proposed which enhances the perceptually invisible image along with preserving its color and naturalness. In this method, the image pixels are grouped into different clusters and are assigned membership values to those clusters. Based on this membership value, its intensity level is modified in the spatial domain. Modification of the gray levels proportional to the membership values leads to the stretching of the image histogram, similar in shape, to the original histogram. The process results in a very small shift in the mean intensity which preserves the color and brightness-related information of the image. The method enhances the image contrast and maintains the naturalness without introducing any artifacts. The simulation results on standard datasets reflect that the proposed algorithm is superior to many state-of-the-art and traditional methods for perceptually invisible images.

Asymmetric Encryption of Invisible Structured Light 3D Imaging

The research proposes a novel invisible structured light 3D object encryption method. The system projects invisible light on the target plane to achieve three-dimensional object reconstruction. The encryption is conducted using keys from eight stripe patterns and two fingerprint patterns using an elliptic curve encryption algorithm to generate eight corresponding ciphertexts. The three-dimensional object is reconstructed using eight fringe patterns with the elliptic curve decryption algorithm. The proposed method greatly reduces the interference of background light in the system to achieve a better 3D imaging accuracy. The elliptic curve cryptosystem is able to ensure 3D object information transmission security. The simulation results validated the robustness and effectiveness of the proposed scheme. The proposed method has practical security-sensitive applications.

No-Reference Metric Optimization-Based Perceptually Invisible Image Enhancement

No-Reference Metric Optimization-Based Perceptually Invisible Image Enhancement