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computer-Generated image Forensics Based on vision Transformer with Forensic Feature Pre-processing Module 6th

Computer-Generated Image Forensics Based on Vision Transfor...

引用

6th International conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Chen, Yifang Wen, Guanchen Wang, Yong Yang, Jianhua Zhang, Yu Guangdong Polytechnic Normal University GuangDong GuangZhou510665 China

ISBN: (纸本)9783031736988

the correct distinction between highly realistic computer-generated (CG) images and photographic (PG) images has become an important area of research. In recent years, most of the CG image forensics methods are proposed based on deep learning, but the detection performances of these methods still need to be improved, especially in terms of robustness and generalization. To tackle these issues, we leverage the vision Transformer (ViT) model, which excels in capturing the global features of images, and design a Forensic Feature Pre-processing (FFP) module to further improve the detection performance. Experiments are conducted on a large-scale CG image benchmark (LSCGB), which is a challenging dataset for CG image detection. the proposed approach can achieve high detection accuracy. Extensive experiments on different public datasets and common post-processing operations demonstrate our approach can achieve significantly better generalization and robustness than the state-of-the-art approaches. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： computer forensics

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Integrating Object Detection in Bird-View Systems for Enhancing Safety in Construction Machinery 6

Integrating Object Detection in Bird-View Systems for Enhanc...

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6th IEEE International conference on image processing, Applications and Systems, IPAS 2025

作者： Hund, Julian Schoneberg, Eric Gorges, Daniel Hashib, Hasan Electrical and Computer Engineering Institute of Electromobility Kaiserslautern Germany

ISBN: (纸本)9798331506520

Working with construction machinery necessitates that workers remain attentive to effectively survey their surroundings. the complex environment, blind spots, and limited visibility while simultaneously tackling complex tasks create hazardous conditions. Camera systems providing a bird view help monitoring the machine's surroundings, but still require active monitoring while working. In this paper, we propose extending bird-view systems with object detection. We investigate and evaluate two different approaches: Detecting people in four fisheye camera images and transforming the obtained bounding boxes into a bird view, and directly detecting people in bird view. Our models, based on the YOLOv5 and YOLOv8 architectures, were trained and validated on custom datasets with 3,302 fisheye images and 1,343 bird-view images. the models achieved mAP@.5-.95 scores of 0.514 (fisheye) and 0.546 (bird view) and run at 25 frames per second on an NVIDIA Jetson Nano edge GPU. Our trained models for the detection utilize the YOLOv5 and YOLOv8 architecture and run inference with 25 frames per second on an NVIDIA Jetson Nano edge GPU. the first approach also addresses objects disappearing in stitching areas - characteristic of bird-view projection errors - by displaying bounding boxes even if they are invisible in bird view. © 2025 IEEE.

关键词： graphics processing unit

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Botanical Bio vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

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6th International conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. this study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. this approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. this research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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Using Hash and Deep Learning for Content-Based image Retrieval 6th

Using Hash and Deep Learning for Content-Based Image Retriev...

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6th International conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Hung, Bui thanh thuy, Tran Xuan Tam, Le Duy Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh Viet Nam

ISBN: (纸本)9789819780891

Given the exponential growth of global data, research attention has increasingly focused on content-based image retrieval. While deep learning has optimized computer vision tasks, existing image search tools continue to face challenges in processing speed. If these issues are resolved, image search has the potential to fully replace traditional search methods. the drawback of prior methods lies in lengthy feature vectors, resulting in time-consuming searches and inconvenience. To mitigate these issues, we employ feature extraction through convolutional neural networks and subsequently apply binary hashing. Experimental results on the fashion-product-images dataset show that using a convolutional neural network combined with binary hashing in image search achieves a mean average precision (MAP 20) of 0.99 and significantly improves the image retrieval time. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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computer vision-Based Weed-Killing Rover 6th

Computer Vision-Based Weed-Killing Rover

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6th International conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Adke, Pallavi Junonikar, Atharva Singh, Ayushi Kanase, Abhimanyu Nair, Prajit Varghese, Anshu Deshmukh, Aditya Pimpri Chinchwad College of Engineering & Research Ravet India

ISBN: (纸本)9789819783281

India's primary industry, agriculture, accounts for the majority of the country's GDP. Weed growth is one of the primary factors that lowers agricultural productivity. In the field, the combination of machine vision and the image processing is showed to be a reliable way for doing point to point real-time detection of weed and for other crop detection, providing with the accurate data to manage the weed tailored to individual sites. the developments in information and electronic technologies are to blame for this. Manual herbicide spraying is one of the most common ways to keep weeds under control. However, this strategy has a lot of negative effects on both agricultural and consumer health. this report summarized the advances in weed detection using open-CV and image detection methods. A detailed explanation was given of the five primary weed identification techniques: firstly preprocessing, secondly feature extraction, third would be segmentation, fourth is extraction, and then lastly classification. the primary difficulty with this model is distinguishing crops from weeds, which can occasionally have identical features and appearances. the model was trained with a sizable dataset in order to address this problem. As a result, this technology will now detect the weed and spray the herbicide in the adequate amount in a targeted manner. this research paper will provide a comprehensive study of various deep learning methods that can be useful for automatic detection and various image processing methodologies. It also provides performance metrics, such as accuracy, and precision for various techniques employed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Machine vision

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6th International conference on Security and Privacy in New Computing Environments, SPNCE 2023

6th International Conference on Security and Privacy in New ...

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6th International conference on Security and Privacy in New Computing Environments, SPNCE 2023

ISBN: (纸本)9783031736988

the proceedings contain 23 papers. the special focus in this conference is on Security and Privacy in New Computing Environments. the topics include: Consortium Blockchain Storage Optimization Based on Fountain Codes;an Incentive Mechanism and An Offline Trajectory Publishing Algorithm Considering Sensing Area Coverage Maximization and Participant Privacy Level;Research on Face Recognition System Based on RLWE Homomorphic Encryption;feedback Feed-Forward Iterative Learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths;open-Closed-Loop Iterative Learning Control Based on Differential Evolution Algorithm for Nonlinear System;overview of Vehicle Edge Computing and Its Security;conFlow: Contrast Network Flow Improving Class-Imbalanced Learning in Network Intrusion Detection;anomaly Detection of Unstable Log Data Based on Contrastive Learning;An Integration-Enhanced ZNN Approach for Chaotic Combination Synchronization with External Disturbances∗;a Lightweight Anomaly Detection Method for Industrial Processes Based on Event Correlation Behavior;a Novel Polar Code-Based Key Encapsulation Mechanism with Non-permutation Equivalent Public Key;two-Stage Multi-lingual Speech Emotion Recognition for Multi-lingual Emotional Speech Synthesis;EncoderMU: Machine Unlearning in Contrastive Learning;NoCrypto: A Web Mining Behavior Detection Method Based on RGB images;image Copy-Move Forgery Detection in the Social Media Based on a Prior Density Clustering and the Point Density;detection of Speech Spoofing Based on Dense Convolutional Network;speech Emotion Recognition Based on Recurrent Neural Networks with Conformer for Emotional Speech Synthesis;Route Privacy-Preserving Authentication Scheme Based on PUF in VANETs;Stable NICE Model-Based Picture Generation for Generative Steganography;computer-Generated image Forensics Based on vision Transformer with Forensic Feature Pre-processing Module;VoIP Steganalysis Using Shallow Multiscale Convolution and

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IPAS 2025 Abstract Page

IPAS 2025 Abstract Page

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International image processing, Applications and Systems conference (IPAS)

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Multichannel Object Detection with Event Camera

Multichannel Object Detection with Event Camera

引用

International image processing, Applications and Systems conference (IPAS)

作者： Rafael Iliasov Alessandro Golkar Chair of Spacecraft Systems Technical University of Munich Munich Germany

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion method (EFM) to enhance object detection in event-based vision systems. Each channel uses a different accumulation buffer to collect events from the event camera. We implement YOLOv7 for object detection, followed by a fusion algorithm. Our multichannel approach outperforms single-channel-based object detection by 0.7% in mean Average Precision (mAP) for detection overlapping ground truth with IOU = 0.5.

关键词： computer vision Event detection Machine vision AI accelerators Object detection Cameras Feature extraction Real-time systems

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Adversarial Encoder-Driven Filter for Targeted image Tagging: A Novel Approach to Visual Content Manipulation

Adversarial Encoder-Driven Filter for Targeted Image Tagging...

引用

International image processing, Applications and Systems conference (IPAS)

作者： Cole Mckee Dominic Flowers Jesse Wood Ethan Shafer Department of Electrical Engineering and Computer Science United States Military Academy West Point New York

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised significant concerns among researchers. this paper addresses the vulnerability of image tagging algorithms, particularly focusing on misclassifications induced by autoencoders. We present experiments conducted on Amazon Rekognition, where we developed a specialized autoencoder to manipulate the latent space, forcing it to align with specific tags. By integrating this manipulated latent space with other images, we demonstrate the ability to increase the confidence of a specific tag on Amazon Rekognition, leading to more false positives of the chosen tag. Our study showcases a practical method to exploit Amazon’s Rekognition image tagging algorithm using a black box approach.

关键词： Training Resistance computer vision Visualization Law enforcement Military computing Computational modeling Autoencoders image annotation Closed box

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

6th National Conference on Computer Vision, Pattern Recognit...

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6th National conference on computer vision, Pattern Recognition, image processing and graphics, NCVPRIPG 2017

ISBN: (纸本)9789811300196

the proceedings contain 48 papers. the special focus in this conference is on computer vision, Pattern Recognition, image processing and graphics. the topics include: Parametric reshaping of humans in videos incorporating motion retargeting;enhanced aggregated channel features detector for pedestrian detection using parameter optimisation and deep features;unsupervised segmentation of speech signals using Kernel-Gram matrices;Design of biorthogonal wavelet filters of DTCWT using factorization of halfband polynomials;single noisy image super resolution by minimizing nuclear norm in virtual sparse domain;near real-time correction of specular reflections in flash images using no-flash image prior;A method for detecting JPEG anti-forensics;an end-to-end deep learning framework for super-resolution based inpainting;saliency map improvement using edge-aware filtering;classification of human actions using 3-D convolutional neural networks: A hierarchical approach;A generative adversarial network for tone mapping HDR images;efficient clustering-based noise covariance estimation for maximum noise fraction;GMM based single depth image super-resolution;patch similarity in transform domain for intensity/range image denoising with edge preservation;multi-modal image analysis for plant stress phenotyping;source classification using document images from smartphones and flatbed scanners;Homomorphic incremental directional averaging for noise suppression in SAR images;An EEG-based image annotation system;multimodal registration of retinal images;Dynamic class learning approach for smart CBIR;SmartTennisTV: Automatic indexing of tennis videos;exploring memory and time efficient neural networks for image captioning;dataset augmentation with synthetic images improves semantic segmentation;deep neural network for foreground object segmentation: An unsupervised approach;document image segmentation using deep features;flow-free video object segmentation.

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