Image Manipulation Research Articles

Forgered Image Perception System using CNN Algorithms

The progression and diversification of methodologies in digital image forensics and manipulation detection are evident in existing work, presenting innovative techniques tailored to specific aspects of forgery detection. These methodologies encompass various challenges such as copy-move forgery, manipulation detection, demosaicing artifacts, image splicing, edge detection, and compression analysis. Together, these studies exemplify ongoing efforts to enhance the accuracy, efficiency, and reliability of forgery detection methods within the constantly evolving landscape of digital image manipulation. The proposed image forgery detection system represents a pioneering advancement, surpassing the limitations of conventional methodologies. It integrates a comprehensive array of detection techniques, including copy-move analysis, color filter array scrutiny, noise variance inconsistency detection, and double JPEG artifact identification. These techniques are bolstered by Convolutional Neural Networks (CNNs). This innovative fusion not only addresses the limitations inherent in existing systems but also signifies a significant leap forward in functionality and efficacy. By synergistically leveraging the strengths of each detection method within a CNN framework, our approach promises heightened accuracy, robustness, and adaptability in discerning even the most sophisticated forms of image tampering. Keywords— Image Tampering, Double JPEG Compression, Demosaicing Artifacts, Recursive Edge Detection, Copy-Move Forgery Detection.

OFIDA: Object-focused image data augmentation with attention-driven graph convolutional networks.

Image data augmentation plays a crucial role in data augmentation (DA) by increasing the quantity and diversity of labeled training data. However, existing methods have limitations. Notably, techniques like image manipulation, erasing, and mixing can distort images, compromising data quality. Accurate representation of objects without confusion is a challenge in methods like auto augment and feature augmentation. Preserving fine details and spatial relationships also proves difficult in certain techniques, as seen in deep generative models. To address these limitations, we propose OFIDA, an object-focused image data augmentation algorithm. OFIDA implements one-to-many enhancements that not only preserve essential target regions but also elevate the authenticity of simulating real-world settings and data distributions. Specifically, OFIDA utilizes a graph-based structure and object detection to streamline augmentation. Specifically, by leveraging graph properties like connectivity and hierarchy, it captures object essence and context for improved comprehension in real-world scenarios. Then, we introduce DynamicFocusNet, a novel object detection algorithm built on the graph framework. DynamicFocusNet merges dynamic graph convolutions and attention mechanisms to flexibly adjust receptive fields. Finally, the detected target images are extracted to facilitate one-to-many data augmentation. Experimental results validate the superiority of our OFIDA method over state-of-the-art methods across six benchmark datasets.

Machine Learning Defenses: Exploring the integration of machine learning techniques within CAPTCHA systems to dynamically adjust challenge difficulty and thwart adversarial attacks

The utilization of machine learning (ML) techniques within CAPTCHA systems represents a significant advancement in cybersecurity, offering dynamic adaptability to thwart evolving adversarial attacks. This research delves into the integration of ML defenses within CAPTCHA frameworks, focusing on their efficacy in adjusting challenge difficulty dynamically to counter sophisticated attacks. By leveraging ML algorithms, CAPTCHA systems can analyze user behavior patterns and adaptively tailor challenges to deter automated bots while ensuring user accessibility and engagement. This paper aims to provide a comprehensive investigation into the design, implementation, and evaluation of ML-based CAPTCHA defenses. It explores various ML approaches, including supervised learning, reinforcement learning, and deep learning, in dynamically adjusting challenge complexity based on user interactions and environmental factors. Furthermore, the study delves into the assessment of ML-driven CAPTCHA systems' resilience against adversarial attacks, such as machine learning-based algorithms, optical character recognition (OCR) techniques, and adversarial image manipulation. Through empirical analysis and experimentation, this research endeavors to elucidate the effectiveness and limitations of ML-based CAPTCHA defenses in real-world scenarios. By elucidating the intricacies of integrating ML techniques within CAPTCHA systems, this paper seeks to contribute valuable insights to the field of cybersecurity, offering guidance for the development of robust and adaptive CAPTCHA mechanisms capable of mitigating emerging threats in the digital landscape.

Image splicing detection using low-dimensional feature vector of texture features and Haralick features based on Gray Level Co-occurrence Matrix

Digital image forgery has become hugely widespread, as numerous easy-to-use, low-cost image manipulation tools have become widely available to the common masses. Such forged images can be used with various malicious intentions, such as to harm the social reputation of renowned personalities, to perform identity fraud resulting in financial disasters, and many more illegitimate activities. Image splicing is a form of image forgery where an adversary intelligently combines portions from multiple source images to generate a natural-looking artificial image. Detection of image splicing attacks poses an open challenge in the forensic domain, and in recent literature, several significant research findings on image splicing detection have been described. However, the number of features documented in such works is significantly huge. Our aim in this work is to address the issue of feature set optimization while modeling image splicing detection as a classification problem and preserving the forgery detection efficiency reported in the state-of-the-art. This paper proposes an image-splicing detection scheme based on textural features and Haralick features computed from the input image’s Gray Level Co-occurrence Matrix (GLCM) and also localizes the spliced regions in a detected spliced image. We have explored the well-known Columbia Image Splicing Detection Evaluation Dataset and the DSO-1 dataset, which is more challenging because of its constituent post-processed color images. Experimental results prove that our proposed model obtains 95% accuracy in image splicing detection with an AUC score of 0.99, with an optimized feature set of dimensionality of 15 only.

AI can empower agriculture for global food security: challenges and prospects in developing nations

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030—Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

Are Tobacco Companies in Nigeria Complying With Health Warning Label Regulations on Cigarettes and Other Tobacco Products?

BACKGROUND: Nigeria has approved 50% coverage of text and images of pictorial health warning labels (PHWLs) on packages of cigarettes and other tobacco products such as cigars, smokeless tobacco, and loose tobacco. While there is knowledge on the extent to which LMICs like Nigeria adopt the WHO Framework Convention of Tobacco Control (FCTC), there is need to examine the compliance with adopted packaging and labeling policies to generate knowledge for strengthening existing policies and efforts at enforcement. We assessed agreement with FCTC regulations as well as compliance with Nigerian requirements which are not adequate and do not effectively capture all FCTC requirements to identify the gaps being explored by these companies in an environment of poor regulation in Lagos, Nigeria. METHODS: This descriptive study was conducted in Lagos State, Nigeria, from October 2022 to January 2023. A multistage random sampling method was utilized. Four local government areas (LGAs) with a combined population of 16 million, accounting for 70% of the population in Lagos, Nigeria were selected while three points‑of‑sale were randomly picked from two selected wards in each LGA. Empty discarded packs of cigarette and other tobacco products were collected from these outlets. The pack dimensions were measured and assessed for PHWLs, health warning manipulations and cessation assistance. Findings were assessed for compliance with the FCTC and national legislation. For each compliance feature, we calculated the frequencies and percentages of the packs that were compliant. RESULTS: 704 packs: 434 packs of 22 cigarette brands and 270 packs of 17 different tobacco products including cigars, snuff and loose tobacco were included in the study. Of 434 cigarette packs assessed, 50.3% were compliant with 9 indicators measured. These ranged from 48.8% to 55.5% for individual brands. For cessation assistance, image variability and image manipulation, compliance was 0% across all cigarette packs. Out of 270 tobacco product packs assessed, 10.6% were compliant with the 9 indicators assessed. Of the 12 multinational parent companies and product distributors assessed, the overall level of compliance among them was 24.9%. Level of compliance was noted to very low for companies involved in marketing other tobacco products, ranging from 0% to 20% but moderate for those marketing cigarettes, ranging from 37% to 55.5%. Companies that had no presence or subsidiaries in Nigeria but only export their products were highly non-compliant. CONCLUSIONS: Tobacco companies had low compliance with PHWL on cigarettes and negligible compliance on other tobacco products like cigars, loose and smokeless tobacco. The government should expand and strictly enforce all regulations applying to health warnings, especially on non-cigarette tobacco products and closely monitor companies that merely export their products to the country.

An Approach towards to Real Time Face Detection using Haar Cascade Algorithm and Machine Learning Technique

Human visual awareness is currently a trending subject within the realm of machine vision. In domains like video monitoring, human-machine interaction, facial identification, and image organization, the initial step often involves locating and recognizing human faces. However, this task can prove challenging. Despite the availability of generic facial images, the processes of facial recognition and expression analysis are necessary. The exploration of computer-based recognition of unbiased facial data remains a relatively uncharted area of study. Facial recognition stands out as one of the remarkable achievements of AI research, captivating the interest of many tech enthusiasts eager to comprehend its underlying mechanisms. Let's delve deeper into this topic to gain insight into its functioning. This study elucidates how deep learning, a crucial component of computer science, can be employed to detect faces using various libraries within OpenCV and Python. The article will propose a cutting-edge technology for real-time human face detection, applicable across a spectrum of platforms including computers, smartphones, and software applications. This approach proves both efficient and effective in detecting faces within images. Furthermore, the article delves into popular OpenCV applications and classifiers utilized in tasks such as image manipulation, facial identification, object detection, and facial recognition. Lastly, various literary assessments are discussed, focusing on applications within computer vision disciplines like facial detection and recognition, as well as identifying emotions such as sorrow, anger, and joy, along with determining gender and age

Using machine learning to predict artistic styles: an analysis of trends and the research agenda

AbstractIn the field of art, machine learning models have been used to predict artistic styles in paintings. The foregoing is somewhat advantageous for analysts, as these tools can provide more valuable results and help reduce bias in the results and conclusions provided. Therefore, the objective of this research was to examine research trends in the use of machine learning to predict artistic styles from a bibliometric review based on the PRISMA methodology. From the search equations, 268 documents were found, out of which, following the application of inclusion and exclusion criteria, 128 documents were analyzed. Through quantitative analysis, a growing research interest in the subject is evident, progressing from user perception approaches to the utilization of tools like deep learning for art studies. Among the main results, it is possible to identify that one of the most used techniques in the field has been neural networks for pattern recognition. Also, a large part of the research focuses on the use of design software for image creation and manipulation. Finally, it is found that the number of studies focused on contemporary modern art is still limited, this is due to the fact that a large part of the investigations has focused on historical artistic styles.

SupCon-MPL-DP: Supervised Contrastive Learning with Meta Pseudo Labels for Deepfake Image Detection

Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains. In addition, the emergence of new generative models capable of producing and editing high-quality images, such as diffusion, consistency, and LCM, poses a challenge for traditional deepfake training models. These advancements highlight the need for adapting and evolving existing deepfake detection techniques to effectively counter the threats posed by sophisticated image manipulation technologies. In this paper, our objective is to detect deepfake videos in unknown domains using unlabeled data. Specifically, our proposed approach employs Meta Pseudo Labels (MPL) with supervised contrastive learning, so-called SupCon-MPL, allowing the model to be trained on unlabeled images. MPL involves the simultaneous training of both a teacher model and a student model, where the teacher model generates pseudo labels utilized to train the student model. This method aims to enhance the adaptability and robustness of deepfake detection systems against emerging unknown domains. Supervised contrastive learning utilizes labels to compare samples within similar classes more intensively, while encouraging greater distinction from samples in dissimilar classes. This facilitates the learning of features in a diverse set of deepfake images by the model, consequently contributing to the performance of deepfake detection in unknown domains. When utilizing the ResNet50 model as the backbone, SupCon-MPL exhibited an improvement of 1.58% in accuracy compared with traditional MPL in known domain detection. Moreover, in the same generation of unknown domain detection, there was a 1.32% accuracy enhancement, while in the detection of post-generation unknown domains, there was an 8.74% increase in accuracy.

EmoStyle: Emotion-Aware Semantic Image Manipulation with Audio Guidance

With the flourishing development of generative models, image manipulation is receiving increasing attention. Rather than text modality, several elegant designs have delved into leveraging audio to manipulate images. However, existing methodologies mainly focus on image generation conditional on semantic alignment, ignoring the vivid affective information depicted in the audio. We propose an Emotion-aware StyleGAN Manipulator (EmoStyle), a framework where affective information from audio can be explicitly extracted and further utilized during image manipulation. Specifically, we first leverage the multi-modality model ImageBind for initial cross-modal retrieval between images and music, and select the music-related image for further manipulation. Simultaneously, by extracting sentiment polarity from the lyrics of the audio, we generate an emotionally rich auxiliary music branch to accentuate the affective information. We then leverage pre-trained encoders to encode audio and the audio-related image into the same embedding space. With the aligned embeddings, we manipulate the image via a direct latent optimization method. We conduct objective and subjective evaluations on the generated images, and our results show that our framework is capable of generating images with specified human emotions conveyed in the audio.

Retraction: "Silencing lncRNA PVT1 inhibits activation of astrocytes and increases BDNF expression in hippocampus tissues of rats with epilepsy by downregulating the Wnt signaling pathway".

Retraction: "Silencing lncRNA PVT1 inhibits activation of astrocytes and increases BDNF expression in hippocampus tissues of rats with epilepsy by downregulating the Wnt signaling pathway", by Teng Zhao, Ying Ding, Min Li, Chunkui Zhou, and Weihong Lin, J Cell Physiol. 2019; 234: 16054-16067: The above article, published online on 25 February 2019 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/10.1002/jcp.28264) has been retracted by agreement between the journal's Editor in Chief, Alexander Hutchison, and Wiley Periodicals LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several flaws and inconsistencies between results presented and experimental methods described were found. Furthermore, indication for image manipulation was found in Figures 3, 7, and 9. Thus, the editors consider the conclusions of this article to be invalid.

Robust multiple focusing through scattering media via feedback wavefront shaping

The control of multiply-scattered light has greatly boosted the understanding of optics in complex photonic systems, leading to many applications including imaging, information processing, random lasing, to name just a few. Here, we demonstrate a real-time feedback approach to realize multiple focusing of multiply-scattered light, which is more robust to the inherent background field compared to the time reversal approach. Our method retrieves the optimized wavefront by aligning the high-dimensional vector of the measured pattern with that of the target pattern. Our theoretical simulations and experimental results show that the feedback approach is capable of constructing homogeneous diffraction-limited spots at predefined positions. Moreover, the peak-to-background ratio exhibits a sub-1/m decay with m being the number of focuses, indicating the excellent capability of the proposed approach to focus scattered light at multiple positions. This efficient approach provides a promising avenue toward complex control on scattered light in complex photonic media, and therefore would be useful in relevant applications such as optical imaging and optical manipulation.

Retraction: miR-655 suppresses epithelial-to-mesenchymal transition by targeting Prrx1 in triple-negative breast cancer.

Zhi-Dong Lv, Bin Kong, Xiang-Ping Liu, Li-Ying Jin, Qian Dong, Fu-Nian Li, Hai-Bo Wang, miR-655 suppresses epithelial-to-mesenchymal transition by targeting Prrx1 in triple-negative breast cancer. Journal of Cellular and Molecular Medicine, 20: 864-873. https://doi.org/10.1111/jcmm.12770 The above article, published online on 28 January 2016 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editor-in-Chief, Stefan Constantinescu, The Foundation for Cellular and Molecular Medicine, and John Wiley and Sons Ltd. In 2022, the authors asked to retract the article as they found that some images were mistakenly duplicated from their previously published papers. Subsequently, an investigation into concerns raised by a third party revealed inappropriate duplications and manipulation of images between this (Figures2B,D, 3A, 5B, 6C,D and 7B) and five other articles (with the same first or corresponding author) that were published previously or in the same year in a different scientific context. Due to the number and the level of errors identified in the published figures, the editors consider the conclusions of this manuscript substantially compromised. Although the authors initially asked for the retraction, they did not agree to the retraction wording.

Retraction Note.

The publisher and Editor in Chief have withdrawn four articles published between 2016 and 2018. These articles were discovered to contain problems, such as image manipulation, absence of ethics approval, and inconsistencies in data presented. This retraction notice pertains to the following articles, with detailed explanations below: - "The effects of MCA-MAO on cAMP pathway in rats with cerebral hemorrhage" by Y. Jiang, X.-F. Wang, Y.-L. Niu, J.-H. Xia-Eur Rev Med Pharmacol Sci 2016; 20 (9): 1834-1838-PMID: 27212177. Readers have pointed out on PubPeer that Figures 1B and D appear to contain some overlaps. Moreover, there is no ethical approval statement for this study. The editorial team has contacted the authors but received no reply. The journal investigation revealed a figure duplication. Therefore, the Editor in Chief mistrusts the results and decided to retract the manuscript. Although the authors were notified of the retraction decision, they remained unresponsive. - "Research on the role of GLP-2 in the central nervous system EPK signal transduction pathway of mice with vascular dementia" by C.-L. Chi, S.-A. Zhang, Z. Liu, M.-X. Chang, H. Wang, Y. Huang-Eur Rev Med Pharmacol Sci 2017; 21 (1): 131-137-PMID: 28121365. Readers have pointed out on PubPeer that Figure 3 appears to contain some overlaps. Moreover, there is no ethical approval statement for this study. The editorial team has contacted the authors but received no reply. The journal investigation revealed a figure duplication. Therefore, the Editor in Chief mistrusts the results and decided to retract the manuscript. Although the authors were notified of the retraction decision, they remained unresponsive. - "Studies on the relationship between P13K/AKT signal pathway-mediated MMP-9 gene and lung cancer" by H. Zheng, J.-F. Liu-Eur Rev Med Pharmacol Sci 2017; 21 (4): 753-759-PMID: 28272723. Readers on PubPeer have expressed concerns about Western blots, as shown in Figure 2. Moreover, there is no ethical approval statement for this study. The editorial team has contacted the authors but received no reply. The journal investigation did not detect figure manipulation; however, since the lack of ethics statement and the lack of the original data, the Editor in Chief decided to retract the manuscript. Although the authors were notified of the retraction decision, they remained unresponsive. - "LncRNA LINC01116 competes with miR-145 for the regulation of ESR1 expression in breast cancer" by H.-B. Hu, Q. Chen, S.-Q. Ding-Eur Rev Med Pharmacol Sci 2018; 22 (7): 1987-1993-DOI: 10.26355/eurrev_201804_14726-PMID: 29687853. Readers on PubPeer have expressed concerns regarding inconsistencies between data presented in Tables and figures, deficiencies in the description of sample collection, and a lack of ethics approval. The editorial team has contacted the authors to request the raw data of the manuscript and a reply to those concerns but has not received any response. Throughout the journal investigation, numerous flaws and inconsistencies were discovered in the presented results. Therefore, the editors deem the conclusions of this article to be invalid and decided to retract the manuscript. The corresponding author did not respond to journal correspondence about the investigation and retraction of this article. These articles have been retracted. The Publisher apologizes for any inconvenience this may cause.

Generation of needle-shaped beam with super-resolution spot and large depth of focus using diffractive optics

The finite depth of focus (DOF) of a focusing lens gives rise to the degradation of lateral resolution at defocused positions. Consequently, the generation of a needle-shaped beam with sub-diffraction size and extended DOF can break through this limitation, but few studies have effectively balanced these two characteristics. In this paper, we propose a method based on the stochastic parallel gradient descent (SPGD) algorithm for designing multi-ring diffractive optical elements (DOEs) to produce the needle-shaped beam, which maintains a super-resolution and uniform spot within a large extended DOF range. Numerical simulations and experimental results have validated that our method can extend the DOF by 14 times longitudinally and reduce the lateral resolution to 73% of Airy spot, while maintaining up to 92% uniformity of the beam diameter. Furthermore, we demonstrate that the DOF of the needle-shaped beam extends with the increase of the number of rings and phase levels within the DOE. Our work provides an effective strategy for the design of DOF-extended DOE and it is anticipated to find potential applications in microscopy and three-dimensional imaging, photolithography, and optical manipulation.

Uncertainty-aware hierarchical labeling for face forgery detection

In this paper, we propose an uncertainty-aware hierarchical labeling method for face forgery detection, which aims to simultaneously explore the traces of forgery contents from a hierarchical level. Unlike existing face forgery detection methods that usually focus on local regions or entire images, our proposed approach takes advantage of hierarchical labeling as auxiliary supervised signals to capture hybrid information from pixel-level, patch-level and image-level. Moreover, we utilize Transformer architecture to extract the patch-level information to build a bridge between the pixel-level and the image-level information. However, the face forgery ground truth always carries data uncertainty due to the existence of the blending step and the compression process during face image manipulation. Thus we introduce an uncertainty learning method to formulate and leverage the data uncertainty. Extensive experimental results on five public datasets demonstrate that our approach not only achieves very competitive performance but also improves the generalization ability.

Severity of Disorganization of Retinal Layers and Visual Function Impairment in Diabetic Retinopathy

Severity of Disorganization of Retinal Layers and Visual Function Impairment in Diabetic Retinopathy

Social media for brand image manipulation: an automotive industry applied approach

The paper illustrates how social media marketing and analytics can assist businesses in achieving branding objectives by increasing their social media impact through advertising, getting social, using appropriate keywords, and creating effective and interactive communication channels with their intended audience. The multifaceted influence of social media postings is demonstrated by leveraging the Toyota Motor Corporation example. An explanation of how defamatory incidents have impacted the company’s social media atmospheric image is also included. Evidence of how negative intervals of social media presence could have been intelligently reversed through efficient content infusions is presented. Among numerous instruments allowing for analysis of marketing strategy results, “Social Mention”, “Talkwalker” and “Mentiolytics” were selected in order to demonstrate the efficiency and utility of social media monitoring methods using freely available tools. In this study, Toyota’s social media marketing strategy is highlighted by presenting measurements of variables such as the reach, strength, passion and sentiment of the brand over randomly selected time windows, demonstrating the dynamically evolving field of social media monitoring techniques.

Robust steganographic framework for securing sensitive healthcare data of telemedicine using convolutional neural network

AbstractData is the key element that runs the modern society. Large amounts of data are being released day by day as a result of many activities. The digital data is transferred through the Internet which may be vulnerable to attacks while transmitting. Especially, the medical data is observed to be of at most importance. Thus, the security of the data should be preserved while communicating through the Internet. Many techniques have been evolved for securing the data, such as Encryption which makes the data not understandable by others and techniques such as Steganography, which hides the data inside other file or document. Traditional methods of Steganography‐like LSB substitution are subjected to risks of manipulation of carrier image and poor embedding capabilities. In light of this, the authors introduce a Convolutional Neural Network‐based image steganography method that operates on DICOM images which conceals the sensitive medical data in the carrier file, thereby increasing the privacy of the sensitive data. The data considered here is brain MRI scan images. The Prep Net, Hiding Net and Reveal Net networks, each with distinct number of convolution layers are used to build the model. The sensitive data is firstly segmented to detect the Region of Interest (ROI) and then the combination of sensitive data and the carrier image are fed to the Prep Net to manipulate sensitive images in such a form that it can easily fit inside the carrier image. The Hiding Net takes the result of Prep Net and embeds the sensitive image inside the carrier image to form a stego image. The Reveal Net takes the result of Hiding Net image and then extracts the sensitive image with minimal loss. The dataset contains medical images of DICOM format taken from open source platform Kaggle that contains 3500 images of 2 classes. The results obtained show that the proposed model achieves 20 percent better output compared to traditional LSB methods. As a result, the potential for hiding the data will be expanded, as well as its security.

Digital Templating of Hip Arthroplasty Using Microsoft PowerPoint: A Pilot Study with Technical Details.

Templating is essential in hip arthroplasty preparation, facilitating implant size prediction and surgical rehearsal. It ensures the selection of suitable implants according to patient anatomy and disease, aiming to minimize post-operative complications. Various templating methods exist, including traditional acetate templating on both analog and digital images, alongside digital templating on digital images, which is categorized into 2D and 3D approaches. Despite the popularity of acetate templating on digital images, challenges such as the requirement for physical templates and result preservation persist. To address these limitations, digital templating with software like OrthoSize and Orthoview has been suggested, although not universally accessible. This technical note advocates for Microsoft PowerPoint as an effective alternative for 2D digital templating, highlighting its user-friendly features for image manipulation without needing specialized software. The described method involves scanning acetate templates, adjusting the images in PowerPoint 365 for size, position, and calibration on patient radiographs, and demonstrating reliability through preliminary assessments, with intraclass correlation coefficient (ICC) values indicating a high level of agreement for cup and stem size (ICC = 0.860, 0.841, respectively) but moderate for neck length (ICC = 0.592). We have introduced a method for performing 2D digital templating in the clinical field without the need for specialized software dedicated to digital templating. We believe this method significantly improves the accessibility to 2D digital templating, which was previously limited by the need for digital templating software. Additionally, it enables surgeons to easily establish arthroplasty plans and share them, overcoming the limitations of acetate templates.