Digital Manipulation Research Articles

SurgEM: A Vision-Based Surgery Environment Modeling Framework for Constructing a Digital Twin Toward Autonomous Soft Tissue Manipulation

SurgEM: A Vision-Based Surgery Environment Modeling Framework for Constructing a Digital Twin Toward Autonomous Soft Tissue Manipulation

Dynamic control of elastic wave transmission by a digital metalayer

Piezoelectric materials with shunt circuits have aroused much research interest due to flexible parameter adjustment. However, shunted piezoelectric elements are difficult to respond to the dynamic changes in the whole system due to the absence of autonomous control ability, which constrains their practical applications. Here, we propose a digital metalayer to control the wave energy transmission across different materials in real time. This digital metalayer comprises a stack of multiple piezoelectric lead zirconate titanate (PZT) disks connected with shunt capacitance circuits (SCCs). The external digital control system adjusts the effective acoustic impedance of the PZT disks through digital potentiometers and microprogrammed control unit, thereby enabling digital manipulation of wave transmission. Utilization of optimized SCCs further enhances adjustment accuracy, supporting both negative and positive capacitance values. The experiments demonstrate that this digital metalayer exhibits remarkable performance in controlling wave transmission. Moreover, the distinct variations in transmitted amplitudes, precisely controlled by the digital metalayer, are harnessed as binary signals for information transmission. An image of letters is encoded into a series of amplitude-modulated waves by the digital metalayer and clearly transmitted. The proposed digital metalayer shows great promise for applications in intelligent impedance matching and the real-time modulation systems.

The Revolution Will (Not?) Be Colourised: Photographs from the Indonesian National Revolution (1945–49) on Social Media

This article critically reflects on the practice of colourisation in present-day recirculations of photographs originally made during the armed Indonesian struggle for independence from Dutch colonial rule (1945–49). The article focuses on digital colour manipulations of photographs disseminated on social media platforms to address ethical and methodological debates dividing academic and amateur historians on the modification of historic photographs. I compare the notable lack of controversy around colourised photographs that show the violence of the Indonesian National Revolution to the heated debates kindled by manipulated images of victims of the Holocaust and the Khmer Rouge regime. ‘Genocide’ arguably remains a special category for determining whether conflict photographs can be modified. I show how Indonesian colourisers fail to interrogate the propaganda origins of the Dutch-made photographs they circulate on social media. For Indonesian social media users, however, deep context and detailed metadata for such photographs are less relevant for generating meaning than debates about how such photographs connect to the unfinished projects of decolonisation in the Netherlands and Indonesia.

A Survey: Digital Image Watermarking-Robustness and Imperceptibility

With the Internet expanding, multimedia security has become a significant problem. As digital image manipulation is getting simpler, the certification of authenticity has become more difficult. Digital watermarking outperforms conventional security methods for protecting photos from attacks. The need for reliable image authentication methods in the contemporary digital era has always been a hot topic among academics and researchers. When transmitting sensitive information today through a regular correspondence route, many hacks are possible. Everyone, therefore, requires their digital data to be non-tampered. Many solutions are available like stenography, cryptography, and watermarking. Watermarking gives promising results when it comes to the authenticity of the images. The robustness and imperceptibility of several existing watermarking techniques are demonstrated in this study and critical analysis is performed. Additionally, the author provides a thorough introduction to key ideas like the transform domain and the spatial domain. This study also offers a thorough analysis of contemporary methods for watermarking digital images while showcasing established practices in various domains. Hence, this research study could potentially serve as a viable resource for researchers seeking to develop effective watermarking techniques for practical applications.

NIR-II light-encoded 4D-printed magnetic shape memory composite for real-time reprogrammable soft actuator

For the intelligent 4D-printed actuators, the excellent performance, including quickly reversible spatial-shape transformation and locking, digital and precise shape manipulation in real-time, and remote actuation in special spaces or harsh environments, is significantly desirable but still challenging. Here, using a UV-curable system containing the shape memory polymer (SMP) and NdFeB particles, namely the magSMP composite, we fabricate a real-time reprogrammable soft actuator via high-resolution Digital Light Processing (DLP)-based 4D printing. The printed structure is composed of an array of physical binary magSMP composite elements (m-bits), analogous to digital bits. Owing to the NdFeB's photothermal effect, each m-bit can be independently and reversibly switched between unlocking or locking states (allowing or prohibiting responsive shape-morphing) in response to the on/off state of NIR-II light. Through projecting NIR-II light patterns for encoding a set of binary instructions onto 4D-printed actuators, the real-time light-programmed deformations are induced precisely under an actuation magnetic field due to the NdFeB's huge coercivity. Thus, the synergistic magnetic and light field-manipulated multimodal deformations of actuators, including mimosa shape changing, grasping, and wire guiding, are achieved. This study shines lights on the fabrication of soft structures of arbitrary sizes and provides their future perspectives in soft robot design.

Anterior odontoid screw fixation of a type III odontoid fracture aided by transoral digital manipulation: illustrative case.

Odontoid process fractures make up 10%-20% of all cervical spine fractures, with type III fractures having a considerable amount of heterogeneity. Most simple type III fractures can be managed nonoperatively. However, 21% of complex type III fractures with significant displacement and angulation are inadequately treated with external immobilization and require surgery. Achieving a reduction via traction can pose a great challenge during intraoperative manipulation, especially when presentation is delayed. A 36-year-old male patient, who presented 2 weeks after a motor vehicle crash, had a Glasgow Coma Scale score of 14 and intact motor and sensory function. A craniocervical computed tomograpy scan revealed a normal brain with a type III odontoid fracture. The patient underwent anterior odontoid screw fixation (AOSF) aided by a transoral digital manipulation to achieve a reduction of the irreducible proximal fracture segment at 8 weeks postinjury. The treatment resulted in preserved neurological function and a satisfactory odontoid fracture reduction. Delayed presentation of a complex type III odontoid fracture can be challenging to treat; however, AOSF with the aid of transoral digital manipulation of the irreducible proximal segment can help to achieve good reduction and fusion with the preservation of neurological function in a young patient. https://thejns.org/doi/10.3171/CASE24294.

Long-term complications and management of dental trauma in the adult patient - Part 2: discoloured, displaced and missing teeth.

The second paper in this two-part review series aims to outline the long-term complications of dental trauma in the adult patient in relation to discoloured, displaced or missing teeth. A brief overview of the cause of discolouration is outlined, followed by assessment and management options. These can include bleaching, veneers, or crown restorations to mask the discolouration. Displacement of teeth from the socket can occur due to luxation injuries. Management can include simple digital manipulation, surgical repositioning, or orthodontic extrusion based on the severity of extrusion and the time from injury. Teeth can be lost early or in the long-term following dental trauma and associated hard and soft tissues deficiencies may also ensue. Replacement options can include a removable partial prosthesis, resin-retained bridge or dental implants. Often, hard and soft tissue augmentation procedures may be required to address any defects and rebuild lost anatomical contours. A thorough assessment followed by the development of a sound management plan which takes patients' general and local factors into account can ensure a predictable and successful treatment outcome. This would end in an aesthetically pleasing and a functionally stable result for the patient.

Exploring the effectiveness of digital manipulation disclosures for Instagram posts on source credibility and authenticity of social media influencers

There is an ongoing discussion on the necessity of implementing disclosures for digitally manipulated pictures within the branded content of social media influencers (SMIs). To address this, we conducted two online survey-based between-subjects experiments to assess the effectiveness of digital manipulation disclosures used on Instagram by an SMI. In the first study (N = 99), we examined how a visual prominent disclosure placed directly on the sponsored posts of an SMI, mediated by the recognition of digital manipulation and the perceived authenticity of the SMI, influenced participants’ intentions toward the SMI, their brand attitude, and their likelihood in engaging in electronic Word-of-Mouth. In the second study (N = 155), we manipulated three conditions: the absence of digital manipulation, the presence of digital manipulation without disclosure, and the presence of digital manipulation with disclosure. We investigated the effects of digital manipulation on the perceived credibility of the SMI and on brand related factors, mediated by the recognition of digital manipulation. Moreover, we explored the moderating effects of product involvement on the relationship between digital manipulation recognition and source credibility. Recognizing digital manipulation has no effects on the perceived authenticity of the SMI. Among social media users with high levels of product involvement who can recognize the digital manipulation, the perceived credibility of the SMI tends to be lower. The research holds both theoretical and practical implications for the field of influencer marketing and disclosure practices.

A simple psychophysical procedure separates representational and noise components in impairments of speech prosody perception after right-hemisphere stroke

After a right hemisphere stroke, more than half of the patients are impaired in their capacity to produce or comprehend speech prosody. Yet, and despite its social-cognitive consequences for patients, aprosodia following stroke has received scant attention. In this report, we introduce a novel, simple psychophysical procedure which, by combining systematic digital manipulations of speech stimuli and reverse-correlation analysis, allows estimating the internal sensory representations that subtend how individual patients perceive speech prosody, and the level of internal noise that govern behavioral variability in how patients apply these representations. Tested on a sample of N = 22 right-hemisphere stroke survivors and N = 21 age-matched controls, the representation + noise model provides a promising alternative to the clinical gold standard for evaluating aprosodia (MEC): both parameters strongly associate with receptive, and not expressive, aprosodia measured by MEC within the patient group; they have better sensitivity than MEC for separating high-functioning patients from controls; and have good specificity with respect to non-prosody-related impairments of auditory attention and processing. Taken together, individual differences in either internal representation, internal noise, or both, paint a potent portrait of the variety of sensory/cognitive mechanisms that can explain impairments of prosody processing after stroke.

Unveiling image forgery with cutting-edge techniques-a survey

Digital social platforms such as Facebook, Instagram, Snapchat, etc., driven by multimedia content, has led to billions of photos being shared annually. The ease of image modification has resulted in ‘deepfakes’, causing potential misinformation, emotional distress, and credibility loss. Image forgery has become a widespread problem with the advent of digital manipulation tools. Manipulated pictures are a problem in the digital world, demanding strong detection methods. This survey explores this important field and offers an extensive overview of state-of-the-art methods for identifying picture forgeries. Breaking down well-known techniques such as classic feature analysis, which unmask significant differences by examining pixels in depth and statistical irregularities. Moreover, this paper explores the revolutionary potential of deep learning by highlighting how convolutional neural networks (CNNs) can analyze pictures with unparalleled accuracy and reveal previously unseen alterations. By providing readers with a comprehensive grasp of the most recent developments in picture forgery detection with the datasets, this article gives them the knowledge they need to successfully negotiate the always changing field of visual deception. This survey acts as a launchpad for further research and development.

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.

Detection of fake face images using lightweight convolutional neural networks with stacking ensemble learning method.

Images and videos containing fake faces are the most common type of digital manipulation. Such content can lead to negative consequences by spreading false information. The use of machine learning algorithms to produce fake face images has made it challenging to distinguish between genuine and fake content. Face manipulations are categorized into four basic groups: entire face synthesis, face identity manipulation (deepfake), facial attribute manipulation and facial expression manipulation. The study utilized lightweight convolutional neural networks to detect fake face images generated by using entire face synthesis and generative adversarial networks. The dataset used in the training process includes 70,000 real images in the FFHQ dataset and 70,000 fake images produced with StyleGAN2 using the FFHQ dataset. 80% of the dataset was used for training and 20% for testing. Initially, the MobileNet, MobileNetV2, EfficientNetB0, and NASNetMobile convolutional neural networks were trained separately for the training process. In the training, the models were pre-trained on ImageNet and reused with transfer learning. As a result of the first trainings EfficientNetB0 algorithm reached the highest accuracy of 93.64%. The EfficientNetB0 algorithm was revised to increase its accuracy rate by adding two dense layers (256 neurons) with ReLU activation, two dropout layers, one flattening layer, one dense layer (128 neurons) with ReLU activation function, and a softmax activation function used for the classification dense layer with two nodes. As a result of this process accuracy rate of 95.48% was achieved with EfficientNetB0 algorithm. Finally, the model that achieved 95.48% accuracy was used to train MobileNet and MobileNetV2 models together using the stacking ensemble learning method, resulting in the highest accuracy rate of 96.44%.

Application of Immersive Media Technology in Ideological and Political Theory Courses Instruction at Shandong Vocational Colleges

This paper describes how virtual classrooms and out-of-classroom instruction can be used to help students in ideological and political theory courses strengthen their identities and improve the efficiency of their assignments. By using immersive media technology, the course is turned into a form of entertainment, enriched with content, and combined with methods such as digital video editing and manipulation to meet the needs of students in university ideological and political theory courses. Virtual reality technology can create realistic virtual environments created by computers, enabling students to perceive and manipulate a variety of virtual objects, interact with them, and create a sense of immersion in the virtual environment. Research has shown the potential benefits of using immersive media technologies and augmented reality in education. The potential of virtual reality in education has been increasingly emphasized and has been widely researched and applied.

Copy Move Image Forgery Detection using Multi-Level Local Binary Pattern Algorithm

Digital image manipulation has become increasingly prevalent due to the widespread availability of sophisticated image editing tools. In copy-move forgery, a portion of an image is copied and pasted into another area within the same image. The proposed methodology begins with extracting the image's Local Binary Pattern (LBP) algorithm features. Two main statistical functions, Stander Deviation (STD) and Angler Second Moment (ASM), are computed for each LBP feature, capturing additional statistical information about the local textures. Next, a multi-level LBP feature selection is applied to select the most relevant features. This process involves performing LBP computation at multiple scales or levels, capturing textures at different resolutions. By considering features from multiple levels, the detection algorithm can better capture both global and local characteristics of the manipulated regions, enhancing the accuracy of forgery detection. To achieve a high accuracy rate, this paper presents a variety of scenarios based on a machine-learning approach. In Copy-Move detection, artifacts and their properties are used as image features and support Vector Machine (SVM) to determine whether an image is tampered with. The dataset is manipulated to train and test each classifier; the target is to learn the discriminative patterns that detect instances of copy-move forgery. Media Integration and Call Center Forgery (MICC-F2000) were utilized in this paper. Experimental evaluations demonstrate the effectiveness of the proposed methodology in detecting copy-move. The implementation phases in the proposed work have produced encouraging outcomes. In the case of the best-implemented scenario involving multiple trials, the detection stage achieved a copy-move accuracy of 97.8 %.

FashionMorph: Contextually Adaptive Clothing Replacement with CLIP, Segmentation, and Stable Diffusion

Abstract: FashionMorph represents a groundbreaking advancement in digital image manipulation, seamlessly blending advanced segmentation techniques with the language comprehension capabilities of CLIP. This innovative approach simplifies the process of precise garment replacements while ensuring the preservation of realistic textures and boundaries. By incorporating stable diffusion, FashionMorph eliminates the need for extensive training on specific clothing styles, offering users a streamlined experience in expressing their preferences through natural language prompts. This empowers users in photo editing, granting them unparalleled creative freedom. The system comprises a comprehensive pipeline designed for the identification and inpainting of clothing elements. Leveraging CLIP and CLIPSeg models for robust image analysis, FashionMorph accurately identifies clothing elements and generates corresponding masks. The inpainting process utilizes a stable diffusion pipeline, resulting in impressive image quality that surpasses the performance of notable models like DCTransformer, StyleGAN, and ADM. Evaluations based on FID and SFID scores, along with Inception Score assessments, further validate FashionMorph's effectiveness in context-aware garment editing. Visual representations of original images, inpainted results, and masks vividly demonstrate the successful outcomes achieved by FashionMorph, establishing it as an efficient and user-friendly solution for professionals and enthusiasts alike. Among the evaluated models, Stable Diffusion emerges as the standout performer, boasting remarkable FID and SFID scores of 0.35 and 0.20, respectively, indicative of its outstanding image fidelity and diversity. Conversely, DCTransformer exhibits the highest FID and SFID scores, while StyleGAN excels in diversity but falls short in FID. ADM (dropout) strikes a balance between fidelity and diversity. These results underscore the exceptional performance of Stable Diffusion, solidifying its position as the leading approach with the lowest FID and SFID values

Cascaded Adaptive Graph Representation Learning for Image Copy-Move Forgery Detection

In the realm of image security, there has been a burgeoning interest in harnessing deep learning techniques for the detection of digital image copy-move forgeries, resulting in promising outcomes. The generation process of such forgeries results in a distinctive topological structure among patches, and collaborative modeling based on these underlying topologies proves instrumental in enhancing the discrimination of ambiguous pixels. Despite the attention received, existing deep learning models predominantly rely on convolutional neural networks (CNNs), falling short in adequately capturing correlations among distant patches. This limitation impedes the seamless propagation of information and collaborative learning across related patches. To address this gap, our work introduces an innovative framework for image copy-move forensics rooted in graph representation learning. Initially, we introduce an adaptive graph learning approach to foster collaboration among related patches, dynamically learning the inherent topology of patches. The devised approach excels in promoting efficient information flow among related patches, encompassing both short-range and long-range correlations. Additionally, we formulate a cascaded graph learning framework, progressively refining patch representations and disseminating information to broader correlated patches based on their updated topologies. Finally, we propose a hierarchical cross-attention mechanism facilitating the exchange of information between the cascaded graph learning branch and a dedicated forgery detection branch. This equips our method with the capability to jointly grasp the homology of copy-move correspondences and identify inconsistencies between the target region and the background. Comprehensive experimental results validate the superiority of our proposed scheme, providing a robust solution to security challenges posed by digital image manipulations.

Moving beyond the Content: 3D Scanning and Post-Processing Analysis of the Cuneiform Tablets of the Turin Collection

This work and manuscript focus on how 3D scanning methodologies and post-processing analyses may help us to gain a deeper investigation of cuneiform tablets beyond the written content. The dataset proposed herein is a key part of the archaeological collection preserved in the Musei Reali of Turin in Italy; these archaeological artefacts enclose further important semantic information extractable through detailed 3D documentation and 3D model filtering. In fact, this scanning process is a fundamental tool for better reading of sealing impressions beneath the cuneiform text, as well as for understanding micrometric evidence of the fingerprints of scribes. Most of the seal impressions were made before the writing (like a watermark), and thus, they are not detectable to the naked eye due to cuneiform signs above them as well as the state of preservation. In this regard, 3D scanning and post-processing analysis could help in the analysis of these nearly invisible features impressed on tablets. For this reason, this work is also based on how 3D analyses may support the identification of the unperceived and almost invisible features concealed in clay tablets. Analysis of fingerprints and the depths of the signs can tell us about the worker’s strategies and the people beyond the artefacts. Three-dimensional models generated inside the Artec 3D ecosystem via Space Spider scanner and Artec Studio software were further investigated by applying specific filters and shaders. Digital light manipulation can reveal, through the dynamic displacement of light and shadows, particular details that can be deeply analysed with specific post-processing operations: for example, the MSII (multi-scale integral invariant) filter is a powerful tool exploited for revealing hidden and unperceived features such as fingerprints and sealing impressions (stratigraphically below cuneiform signs). Finally, the collected data will be handled twofold: in an open-access repository and through a common data environment (CDE) to aid in the data exchange process for project collaborators and common users.

Exposing Deep Fake Face Detection using LSTM and CNN

The rapid advancement of deep learning techniques, creating realistic multimedia content has become increasingly accessible, leading to the proliferation of DeepFake technology. DeepFake utilizes generative deep learning algorithms to produce or modify face features in a highly realistic manner, often making it challenging to differentiate between real and manipulated media. This technology, while beneficial in fields such as entertainment and education, also poses significant threats, including misinformation and identity theft. Consequently, detecting DeepFakes has become a critical area of research. In this paper, we propose a novel approach to DeepFake face detection by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. Our method leverages the strengths of CNNs in spatial feature extraction and LSTMs in temporal sequence modeling to enhance detection accuracy. The CNN component captures intricate facial features, while the LSTM analyzes the temporal dynamics of video frames. We evaluate our model on several benchmark datasets, including Celeb-DF (v2), DeepFake Detection Challenge Preview, and FaceForensics++. Experimental results demonstrate that our hybrid CNN-LSTM model achieves state-of-the-art performance, surpassing existing methods in both accuracy and robustness. This study highlights the potential of combining CNN and LSTM architectures for effective DeepFake detection, contributing to the ongoing efforts to safeguard against digital media manipulation

Safe and genotyped. A non-invasive method for extraction of amphibian DNA from water baths and its application on Northern spectacled salamanders, Salamandrina perspicillata (Savi 1821)

The global decline of amphibian populations, with 40.7% of species classified as threatened, calls for innovative and ethical approaches in conservation genetics. Molecular biology advancements have introduced environmental DNA (eDNA) analysis, primarily focusing on aquatic environments. However, the present study explores a novel non-invasive protocol using water samples to extract DNA from terrestrial and semi-terrestrial amphibians, specifically the endangered Italian endemic salamander, Salamandrina perspicillata (Savi, 1821). Unlike traditional invasive methods involving tissue sampling, this protocol immerses animals briefly, eliminating the need for digit or tail amputations or manipulation for buccal swabs. The study validated the protocol through DNA extraction, amplification, and sequencing, yielding results comparable to traditional methods. The non-invasive nature of the protocol aligns with the 3Rs principles (Replace, Reduce, Refine) and offers a streamlined, stress-minimizing alternative for studying protected and endangered species. Future experiments should also explore further refinements, including reduced soaking times and additional applications, such as skin microbiota analysis. This protocol represents a significant step towards ethical and effective research practices in amphibian conservation genetics, encouraging a paradigm shift in wildlife research ethics. Continued innovation in non-invasive methodologies is essential for comprehensive understanding and robust conservation strategies amid the ongoing biodiversity crisis.

Face Manipulation Detection

The rise of digital manipulation techniques has led to the proliferation of deepfakes and other manipulated facial images, posing significant challenges to online trust and security. This paper proposes a novel deep learning model aimed at efficiently and effectively detecting face manipulations. The architecture combines the strengths of Efficient Net, a convolutional neural network (CNN) renowned for its accuracy and efficiency, with Long Short-Term Memory (LSTM) networks. Efficient Net is utilized for extracting high-level features from facial images, enabling the model to capture subtle inconsistencies that may indicate manipulation. These features serve as crucial inputs to the subsequent analysis performed by LSTM networks. LSTMs excel at capturing temporal dependencies within sequences of data, making them particularly well-suited for detecting manipulations in video sequences. By leveraging the power of CNNs for feature extraction and the sequential learning capabilities of LSTMs, the proposed hybrid approach aims to achieve superior performance in face manipulation detection. This combination allows the model to effectively analyse both spatial and temporal aspects of facial images, enhancing its ability to detect various forms of manipulation accurately