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image Steganalysis based on Pretrained Convolutional neural networks 18

Image Steganalysis based on Pretrained Convolutional Neural ...

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18th ieeE International colloquium on Signal processing and applications (CSPA)

作者： Ahmed, Ismail Taha Hammad, Baraa Tareq Jamil, Norziana Univ Anbar Coll Comp Sci & Informat Technol Anbar Iraq Univ Tenaga Nas Coll Comp & Informat Kajang Malaysia

ISBN: (纸本)9781665485296

the process of identifying the presence of secret information in cover images is known as image steganalysis. As a result, classifying an image as a cover image or a stego image might be considered a classification task. The majority of steganalysis approaches that rely on deep learning are effective. Deep learning technology can identify and extract features mechanically using deep networks, allowing steganalysis technology to eliminate the need for specialist knowledge. However, Deep learning model training is tough and takes a large amount of processing time and information. Therefore, pre-trained CNN such as AlexNet model were used as feature extractors to save time during training. Therefore, this research presented an image steganalysis method based on AlexNet CNN Model. There are 3 steps make up the proposed image steganalysis method: Firstly, Data collection and preparation. Secondly, AlexNet model are used for extract Distinctive features. Lastly, the feature vector is then utilized to train the Random forest (RF) classifier in order to detect the binary classification (Cover/Stego). The experimental results under IStego100K database show that the proposed method accuracy is 99%. The properties of AlexNet models can be deduced to be useful and concise to classify using RF. In compared to previous techniques, the presented method outperformed them.

关键词： image Steganalysis CNN AlexNet CNN Model Random forest (RF) Classifier IStego100K

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CNN Model Compression by Merit-Based Distillation 19

CNN Model Compression by Merit-Based Distillation

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19th ieeE International colloquium on Signal processing and Its applications, CSPA 2023

作者： Morikawa, Takumi Kameyama, Keisuke University of Tsukuba Degree Programs in Systems and Information Engineering Graduate School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9781665476928

Deep learning has advanced dramatically in recent years, and especially large convolutional neural networks (CNNs) have shown outstanding performance in a wide variety of tasks. However, such large-scale CNNs may not be able to be utilized in practice because they require a lot of computational resources. Therefore, there is a need to realize a CNN in a small model that has high performance similar to the large CNN. One method to solve these problems is Distillation, which uses a model of large scale and high performance as the teacher and a small model as the student model, and performs model compression by transferring the knowledge of the teacher model to the student model. However, in distillation, even when the teacher model incorrectly guesses the training input, the student model learns to mimic the teacher. This can prevent the student model from learning better. In this paper, we propose Meritbased Distillation, in which the accuracy of the teacher model's output determines the degree to which the student model mimics the teacher model's output, thereby preventing the student to learn undesired mappings. In the experiment, the effect of the Merit-based Distillation is evaluated. Consequently, the proposed method outperforms the improvements in the generalization by the student models trained by other known methods. © 2023 ieeE.

关键词： image classification

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Vision Transformers: A Review of Architecture, applications, and Future Directions 7

Vision Transformers: A Review of Architecture, Applications,...

引用

7th ieeE International Congress on Information Science and Technology, CiSt 2023

作者： Berroukham, Abdelhafid Housni, Khalid Lahraichi, Mohammed Ibne Tofail University Faculty of Science Department of Computer Science Kenitra Morocco

ISBN: (纸本)9781665461337

In recent years, the development of deep learning has revolutionized the field of computer vision, especially the convolutional neural networks (CNNs), which become the preferred approach for numerous tasks handling images. However, CNNs have difficulty interpreting massive and complicated datasets, which has led to the creation of alternative architectures such as vision transformers. The transformer architecture, which was initially developed for natural language processing (NLP), is modified for image-related applications via vision transformers. In this paper, we present an outline of the main concepts and components of vision transformers. We review various variations and modifications to the architecture, and compare different approaches based on their effectiveness, complexity, and other attributes. Additionally, we examine the applications and uses of vision transformers, such as image classification, object detection, and semantic segmentation, and provide illustrations of relevant real-world situations. Finally, we discuss the potential impact of vision transformers on computer vision, while exploring the challenges and restrictions associated with their usage. We conclude by outlining potential new directions and advancements in the field of computer vision, as well as areas that require further study and investigation. © 2023 ieeE.

关键词： Computer vision

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Key Area Acquisition Training for Practical image-based Plant Disease Diagnosis 18

Key Area Acquisition Training for Practical Image-based Plan...

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18th ieeE International colloquium on Signal processing and applications (CSPA)

作者： Odagiri, Kaito Shibuya, Shogo Cap, Quan Huu Iyatomi, Hitoshi Hosei Univ Grad Sch Sci & Engn Appl Informat Tokyo Japan

ISBN: (纸本)9781665485296

Automatic diagnosis of plant diseases using images is a fine-grained task, and disease symptoms are often ambiguous and highly variable. Pre-extraction of the region of interest (ROI) exhibiting disease symptoms (such as one or more leaves) is known to have a certain effect on improving accuracy. However, the ROI extraction at runtime is time-consuming, resulting in issues of system usability. This paper proposes a new training method called key area acquisition training (KAAT). KAAT reduces the variation in prediction results between images before and after the extraction of the ROI. By directing the model's attention to the ROI through learning, KAAT contributes to improved diagnostic performance without sacrificing execution time during diagnosis. In the evaluation, we conducted nine class diagnosis task (eight diseases and healthy) using 77K and 9K images of cucumber leaves (collected from different fields) for training and testing, respectively. The proposed KAAT improved diagnostic accuracy by 3.8% in macro-F1 and 2.0% in micro accuracy without increasing execution time.

关键词： automated plant diagnosis stability training convolutional neural networks segmentation ROI

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Multi-Class Classification of Lung Cancer Using Streamlined Convolutional neural networks

Multi-Class Classification of Lung Cancer Using Streamlined ...

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International colloquium on Signal processing & Its applications, CSPA

作者： Yu Tim Goh Hui Na Chua Muhammed Basheer Jasser Richard T.K. Wong Bayan Issa School of Engineering and Technology Sunway University No.5 Jalan Universiti Selangor Darul Ehsan Malaysia Faculty of Informatics Engineering University of Aleppo Syria

ISBN: (数字)9798331522193

ISBN: (纸本)9798331522209

Lung cancer remains one of the leading causes of cancer-related morbidity and mortality worldwide, highlighting the importance of accurate and timely diagnosis. Medical imaging, particularly Computed Tomography (CT), plays a pivotal role in lung cancer detection due to its ability to capture high-resolution, cross-sectional views of lung tissue. However, interpreting CT scans manually is prone to inter-operator variability and fatigue. This paper proposes a deep learning approach, Convolutional neural Network (CNN), to classify lung CT images into four categories: normal, adenocarcinoma, squamous cell carcinoma, and large cell carcinoma. Our proposed method relies on streamlined hyperparameters, including batch normalization, dropout, L2 regularization, and data augmentation strategies, to handle imbalanced classes and improve generalization. We conducted experiments using varying image resolutions and train-validation-test splits for model performance evaluation. The best model achieved a 0.96 classification accuracy rate at 192x192 image size with macro average precision, recall, specificity, and F1-score of 0.96, 0.96, 0.99 and 0.96, respectively. Our research findings underscore the potential of streamlined CNN architectures to achieve robust model performance comparable to more computationally demanding methods, thus facilitating broader adoption in medical imaging applications. This study also highlights the impact of different image resolutions and parameter choices, offering a practical solution for clinicians and data scientists with limited resources.

关键词： image resolution Accuracy Computed tomography Lungs Computational modeling Lung cancer Computer architecture Streaming media Convolutional neural networks Biomedical imaging

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Universal Robust Vehicle Identification System

Universal Robust Vehicle Identification System

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International colloquium on Signal processing & Its applications, CSPA

作者： Lorenz Joshua S. Alfonso Tiffany Joy F. Benitez Cseanne Jaycons O. Cabalquinto Francis Jerome T. Perez Janet Yang Engr. Leonard U. Ambata Department of Electronics and Communications Engineering De La Salle University Manila 2401 Taft Ave. Malate Manila Philippines

With the exponential rise in vehicular traffic volume, an intelligent system that is able to detect and classify would be essential. image processing has already placed its significance for real-world applications on machine learning, one of which is traffic analysis. Through this study, the researchers developed a system that allows vehicular tracking and identification using the methods of neural networks for object detection, specifically the YOLOv5 algorithm. The system training utilized an initial dataset generated by the researchers that eliminated the manual intervention of image annotation. The developed system was intervened under the conditions of two different locations. It showed its detection and recognition capabilities through the integrated system features, namely, counting, vehicle type classification, and traffic condition assessment. A region of interest (ROI) line was the primary basis for counting, and a lane-per-lane evaluation was displayed. These features were analyzed individually, between the actual and detected and wrong and correct detections. The increasing trend on accuracy proves the efficiency of the fine-tuning process implemented on the system implementation except for the color that requires a separate training process. After the last finetuning process, an accuracy of 92.57% for vehicle class, 93% for type identification, 82% for color identification and 96.67% for detection count were achieved in the final video run.

关键词： Training Machine learning algorithms image color analysis neural networks Object detection Manuals Feature extraction

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image Pre-processing Significance on Regions of Impact in a Trained Network for Facial Emotion Recognition 11th

Image Pre-processing Significance on Regions of Impact in a ...

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11th IFAC Symposium on Biological and Medical Systems (BMS)

作者： Arabian, H. Wagner-Hartl, V Chase, J. Geoffrey Moeller, K. Inst Tech Med ITeM D-78054 Villingen Schwenningen Germany Hsch Furtwangen Univ Dept Ind Technol D-78532 Tuttlingen Germany Univ Canterbury Ctr Bioengn Dept Mech Engn Christchurch New Zealand

Facial emotion recognition (FER) has gained interest and focus over the years. It can be useful in many different applications and could offer significant benefit as part of feedback systems to help train children with Autism Spectrum Disorder (ASD) who struggle to recognize facial expressions and emotions. This paper explores the effectiveness and significance of image pre-processing in neural networks on developing suitable models for classification. Transfer Learning using the popular "AlexNet" architecture was used in the development of the model with three different approaches for image inputs. Model performance was compared using accuracy of randomly selected validation set after training on a different random training set from the Oulu-CASIA database and visualizations of predicted areas of importance analyzed. image classes were distributed evenly, and accuracies of up to 99.90% were observed with small variation between approaches but significant difference in regions of impact. The visualization process highlighted the importance of image pre-processing prior to network training to improve accuracy and eventual efficacy for this application in ASD. Copyright (C) 2021 The Authors.

关键词： Autism Spectrum Disorder (ASD) Facial Emotion Recognition (FER) AlexNet Oulu-CASIA Network Prediction Visualization

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An Optimized Classification Model for Coffea Liberica Disease using Deep Convolutional neural networks 16

An Optimized Classification Model for Coffea Liberica Diseas...

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16th ieeE International colloquium on Signal processing and its applications (CSPA)

作者： Montalbo, Francis Jesmar P. Hernandez, Alexander A. Technol Inst Philippines Grad Programs Manila Philippines Technol Inst Philippines Coll Informat Technol Educ Manila Philippines

ISBN: (纸本)9781728153100

The Philippines gave coffee a unique identity and taste in the form of Barako coffee, a variant that came from the species of Coffea liberica. However, the crop yielded faces a challenge in coping up with several widespread diseases leading it to lose in quantity and quality. Barako became a less prioritized crop compared to Excelsa, Robusta, and Arabica. The troublesome method of identifying diseases like rust, spots, and insect infestation caused many losses for farmers due to improper diagnosis and treatment. This research aims to apply deep learning methods to alleviate the problem. A deep convolutional neural network was optimized to perform the difficult task of classifying diseases to assist farmers in applying an appropriate treatment. With several experiments, this study develops several models to distinguish the best possible model that can yield the most accurate results. This study acquired 3958 high-resolution images to train the desired model. The result achieved a 100 percent accuracy rate while other models misclassified due to overfitting problems. Proper tuning of hyperparameters can address overfitting together with an appropriate choice of optimizers resulting in an efficient classifier. Moreover, this research identified potential future research works to apply the model for real-life scenarios.

关键词： deep learning deep convolutional neural networks plant disease Barako coffee hyperparameter optimization image classification

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Developing a Flood Risk Assessment Using Support Vector Machine and Convolutional neural Network: A Conceptual Framework 15

Developing a Flood Risk Assessment Using Support Vector Mach...

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ieeE 15th International colloquium on Signal processing and its applications (CSPA)

作者： Opella, Joe Marlou A. Hernandez, Alexander A. Technol Inst Philippines Manila Philippines

ISBN: (纸本)9781538675632

Flooding is one of the most devastating natural hazards that affect not only to infrastructures and agriculture but also to human lives. The prominent effect of global warming boasted its danger and impact in a wider range. In order to address and provide more effective measures to lessen the impact of flood hazards, it would be better to identify first the areas with such flood vulnerability. The proposed study aims to exploit the data available from the Geographical Information System (GIS) and the technology advancement in the modern world in producing a reliable flood susceptibility and probability map. Fusing ConvNet, a feedforward neural networks that specialize in image processing and prediction with SVM, a supervised machine learning for classification and regression analysis for a better image map results. Distinct image prediction output from dilated convolution and deconvolution network will be used as an input to SVM in producing its final output.

关键词： Support Vector Machine ConvNet Flood susceptibility Flood Assessment Non-linear neural Network

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Super Resolution of Car Plate images Using Generative Adversarial networks 15

Super Resolution of Car Plate Images Using Generative Advers...

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ieeE 15th International colloquium on Signal processing and its applications (CSPA)

作者： Lai, Tan Kean Abbas, Aymen F. Abdu, Aliyu M. Sheikh, Usman U. Mokji, Musa Khalil, Kamal Univ Teknol Malaysia Sch Elect Engn Fac Engn Skudai 81310 Johor Malaysia

ISBN: (纸本)9781538675632

Car plate recognition is used in traffic monitoring and control systems such as intelligent parking lot management, finding stolen vehicles, and automated highway toll. In practice, Low-Resolution (LR) images or videos are widely used in surveillance systems. In low resolution surveillance systems, the car plate text is often illegible. Super-Resolution (SR) techniques can be used to improve the car plate quality by processing a series of LR images into a single High-Resolution (HR) image. Recovering the HR image from a single LR is still an ill-conditioned problem for SR. Previous methods always minimize the mean square loss in order to improve the peak signal to noise ratio (PSNR). However, minimizing the mean square loss leads to overly smoothed reconstructed image. In this paper, Generative Adversarial networks (GANs) based SR is proposed to reconstruct the LR images into HR images. Besides that, perceptual loss is proposed to solve the smoothing issue. The quality of the GAN based SR generated images is compared to existing techniques such as bicubic, nearest and Super-Resolution Convolution neural Network (SRCNN). The results show that the reconstructed images using GANs based SR achieve better results in term of perceptual quality compared to previous methods.

关键词： Super resolution car plate generative adversarial networks

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