Image Quality Detection Using The Siamese Convolutional Neural Network

Vizvary, Ladislav; Sopiak, Dominik; Oravec, Milos; Bukovcikova, Zuzana

doi:10.1109/elmar.2019.8918678

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Siamese neural networks focus on tasks that require identifying, comparing, and relating patterns. The design of this neural network model consists of two branches or similar models that share parameters, making it a suitable option for the detection of tampered areas [30,31]. The main advantage of Siamese neural networks is that these models can be used to determine the similarity between two images by comparing them, which is useful for identifying manipulated areas.…”

Section: Siamese Neural Networkmentioning

confidence: 99%

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

Ramirez-Rodriguez,

Arevalo-Ancona,

Perez-Meana

et al. 2024

Applied Sciences

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The exponential surge in specialized image editing software has intensified visual forgery, with splicing attacks emerging as a popular forgery technique. In this context, Siamese neural networks are a remarkable tool in pattern identification for detecting image manipulations. This paper introduces a deep learning approach for splicing detection based on a Siamese neural network tailored to identifying manipulated image regions. The Siamese neural network learns unique features of specific image areas and detects tampered regions through feature comparison. This architecture employs two identical branches with shared weights and image features to compare image blocks and identify tampered areas. Subsequently, a K-means algorithm is applied to identify similar centroids and determine the precise localization of duplicated regions in the image. The experimental results encompass various splicing attacks to assess effectiveness, demonstrating a high accuracy of 98.6% and a precision of 97.5% for splicing manipulation detection. This study presents an advanced splicing image forgery detection and localization algorithm, showcasing its efficacy through comprehensive experiments.

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Section: Siamese Neural Networkmentioning

confidence: 99%

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

Ramirez-Rodriguez,

Arevalo-Ancona,

Perez-Meana

et al. 2024

Applied Sciences

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“…Following these calculations, we may compare the two photos to see whether they are comparable. Face recognition, signature verification, and object tracking have been effectively accomplished using Siamese CNN in computer vision [16]- [22]. This study develops a building application of facial recognition systems using a Siamese CNN and the Kivy framework.…”

Section: Introductionmentioning

confidence: 99%

Face recognition based on Siamese convolutional neural network using Kivy framework

Aufar¹,

Sitanggang²

2022

IJEECS

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Human face recognition is a vital biometric sign that has remained owing to its many levels of applications in society. This study is complex for free faces globally because human faces may vary significantly due to lighting, emotion, and facial stance. This study developed a mobile application for face recognition and implemented one of the convolutional neural network (CNN) architectures, namely the Siamese CNN for face recognition. Siamese CNN can learn the similarity between two object representations. Siamese CNN is one of the most common techniques for one-shot learning tasks. Our participation in this study determined the efficiency of the Siamese CNN architecture with the enormous quantity of face data employed. The findings demonstrated that the suggested strategy is both practical and accurate. The method with augmentation produces the best results with a total data set of 9000 face images, a buffer size of 10000, and epochs of 5, producing the minimum loss of 0.002, recall of 0.996, the precision of 0.999, and F1-score of 0.672. The proposed method gets the best accuracy of 98% with test data. The Siamese CNN model is successfully implemented in Python, and a user interface and executables are built using the Kivy framework.

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Colorization of Grayscale Images Using Convolutional Neural Network and Siamese Network

Kumar¹,

George²,

Binu³

2022

Algorithms for Intelligent Systems

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Image Quality Detection Using The Siamese Convolutional Neural Network

Cited by 4 publications

References 6 publications

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks

Face recognition based on Siamese convolutional neural network using Kivy framework

Colorization of Grayscale Images Using Convolutional Neural Network and Siamese Network

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