Article
Computer Science, Information Systems
Nam Thanh Pham, Chun-Su Park
Summary: In the last decades, deep learning has become a powerful technique for solving challenging problems, including image forgery detection. This article provides a comprehensive survey of state-of-the-art deep learning methods for detecting tampered regions in images, focusing on copy-move and spliced images. The surveyed techniques leverage efficient deep learning methods such as CNN, RCNN, and LSTM to adapt to the detection of tampered traces, outperforming traditional non-deep learning approaches.
Article
Computer Science, Information Systems
Mayank Verma, Durgesh Singh
Summary: In this digital era, the internet is flooded with a massive amount of images, which are widely used for digital communications and considered a valuable source of information in various fields. However, due to the availability of digital image editing software, these images can be easily manipulated without leaving any traces. Therefore, it is crucial to validate the integrity of the images. One of the most common and popular manipulation techniques is Copy Move Forgery (CMF), where a portion of an image is copied and pasted to another region within the same image. This paper reviews recent state-of-the-art CMF detection schemes, their advantages, disadvantages, and performance evaluation criteria, as well as the image datasets used for CMF detection and their merits and demerits. Additionally, the paper discusses the challenges, issues, and future directions in the field of CMF detection, aiming to provide researchers with a comprehensive understanding of advancements in CMF detection techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Engineering, Multidisciplinary
Anjani Kumar Rai, Subodh Srivastava
Summary: Image forging refers to the alteration of a digital image to hide important information, which can be difficult to distinguish from the original image. The demand for authentic and intact images has driven the development of detection methods for fabricated images. This work provides a comprehensive analysis of digital image forgery detection techniques, including their steps and effectiveness. It helps identify and classify different types of forgery and compares them with state-of-the-art methods. The research also highlights current issues and potential future directions in this field.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Computer Science, Information Systems
Marcello Zanardelli, Fabrizio Guerrini, Riccardo Leonardi, Nicola Adami
Summary: In recent years, there has been a proliferation of fake and altered images due to the availability and ease of use of image editing tools. This paper conducts a survey of the latest image forgery detection methods based on Deep Learning (DL) techniques, focusing on copy-move and splicing attacks. The survey discusses the key aspects of these methods, the datasets used for training and validation, as well as their performance. The paper also addresses future research trends and directions in deep learning architectures and evaluation approaches, as well as dataset building for easy methods comparison.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Anjali Diwan, Upasana Sonkar
Summary: Multimedia forensics is an essential field of research that focuses on verifying and analyzing the authenticity of multimedia content in the digital world. This paper provides an overview of current techniques in multimedia manipulation detection, specifically in image, video, and audio analysis. The paper also discusses the limitations of current techniques and highlights future research directions in the field.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Fatemeh Zare Mehrjardi, Ali Mohammad Latif, Mohsen Sardari Zarchi, Razieh Sheikhpour
Summary: The development and availability of digital devices have made image manipulation a prevalent issue. The use of deep learning methods in forgery detection has gained popularity due to their automatic identification and robustness. This study provides a comprehensive review of image forgery types, evaluation metrics, and detection methods, with a focus on deep learning.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Syed Tufael Nabi, Munish Kumar, Paramjeet Singh, Naveen Aggarwal, Krishan Kumar
Summary: With the rise of the Internet, the authenticity of images and videos has become a significant challenge. The availability of manipulation tools online makes it easier for criminals to tamper with evidence in the form of images and videos. Traditional forgery detection tools struggle to identify such manipulations. Researchers are faced with the task of developing a generic tool to efficiently detect various types of forgeries.
MULTIMEDIA SYSTEMS
(2022)
Article
Computer Science, Information Systems
Mehmet Elmaci, Ahmet Nusret Toprak, Veysel Aslantas
Summary: In this paper, a novel two-stream CNN method is proposed for background forgery detection. The proposed method directly addresses the common image forgery technique of background replacement. It achieves an accuracy of 95.5% in detecting background forgeries, improving the accuracy by over 14% compared to prior works, as demonstrated by experiments using a novel dataset.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Nitin Arvind Shelke, Singara Singh Kasana
Summary: Digital videos are widely spread through social networking websites, and the availability of editing tools has made it easier to modify video content. Video forgery detection aims to identify manipulations in videos and verify their authenticity, with techniques categorized as active and passive.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Simranjot Kaur, Sumit Chopra, Anchal Nayyar, Rajesh Sharma, Gagandeep Singh
Summary: This article presents a novel method for detecting digital image forgery using a deep convolutional neural network. Multiple experiments on the COVERAGE dataset have resulted in good performance, surpassing state-of-the-art techniques.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Multidisciplinary
Dilip Kumar Sharma, Bhuvanesh Singh, Saurabh Agarwal, Lalit Garg, Cheonshik Kim, Ki-Hyun Jung
Summary: This paper discusses the spread of fake images on social media platforms, their impact on individuals, organizations, and governments, and the challenges and methods for detecting fake images. The importance of multimodal approaches and existing datasets and evaluation tools are emphasized. The paper concludes by highlighting the future directions of fake image detection research and the importance of interdisciplinary collaboration.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Moataz Z. Salim, Ali J. Abboud, Remzi Yildirim
Summary: This paper proposes a block-based approach to protect the integrity of digital images by detecting and localizing forgeries. By employing a visual cryptography-based watermarking approach, this method is able to detect and localize image forgeries with high accuracy and robustness, as demonstrated in the experiments.
Article
Computer Science, Information Systems
Priya Mariam Raju, Madhu S. Nair
Summary: Digital images are widely used in various fields, and the credibility of image content is becoming increasingly important. A method proposed for copy-move forgery detection integrates traditional and new techniques to improve detection accuracy of forged regions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Shahela Saif, Samabia Tehseen
Summary: Deep learning has enabled the creation of deepfakes in computer vision, which pose a threat to legal, political, and social systems. Research is being conducted to develop detection methods to combat deepfake content and preserve privacy. This study aims to provide insights into deepfake creation techniques and detection methods for developing effective solutions.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Anjali Diwan, Dinesh Kumar, Rajesh Mahadeva, H. C. S. Perera, Janaka Alawatugoda
Summary: The authentication of digital images is a challenging task due to the various image forgery techniques used, including copy-move forgery. This paper introduces a novel approach using SuperPoint, a self-supervised image keypoint detector, to accurately detect copy-move forgery by combining keypoint detection and descriptor extraction. The proposed approach handles images with different textures and produces stable results, making it a functional and reliable tool for detecting copy-move forgery in a diverse range of forged images. Comparative analysis shows the superior performance of our approach, and its computational efficiency enables real-time forgery detection. Our approach using SuperPoint offers an effective solution for image forensics and authenticity.
Article
Computer Science, Information Systems
S. Devi Mahalakshmi, K. Vijayalakshmi, S. Priyadharsini
DIGITAL INVESTIGATION
(2012)
Article
Computer Science, Artificial Intelligence
S. Devi Mahalakshmi, K. Vijayalakshmi
Summary: Irregular climatic patterns and environmental issues can lead to pests affecting crops, requiring timely identification and action. Farmers often struggle to monitor crop pests in a timely manner, and the correct use of chemicals is also a challenge.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
S. Devi Mahalakshmi
Summary: This paper proposes a kidney stone classification method based on optimized Transfer Learning (TL). Image processing techniques and Deep Convolutional Neural Network models are used for classification. By employing ensemble learning and a metaheuristic algorithm, the classification performance is improved.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2023)
Article
Imaging Science & Photographic Technology
A. Meenakshi, A. P. Janani, S. Devi Mahalakshmi, S. Vanitha Sivagami
Summary: Due to the growth of image exploitation, a Content-Based Fabric Image Retrieval (CBFIR) method is proposed in this paper that is based on texture and colour features extraction. The Fuzzy C-Means (FCM) algorithm creates primary clusters and the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm updates every cluster in the dictionary. The proposed method's performance is sufficiently good with a recall rate of 97%, accuracy of 91%, and precision of 88%.
IMAGING SCIENCE JOURNAL
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
S. Devi Mahalakshmi, B. Chandra Mohan
INTELLIGENT COMPUTING AND COMMUNICATION, ICICC 2019
(2020)
Proceedings Paper
Engineering, Electrical & Electronic
S. T. Suryakanthi Sornalatha, S. Devi Mahalakshmi, K. Vijayalakshmi
2015 2ND INTERNATIONAL CONFERENCE ON ELECTRONICS AND COMMUNICATION SYSTEMS (ICECS)
(2015)
Proceedings Paper
Computer Science, Information Systems
S. Devi Mahalakshmi, K. Vijayalakshmi, K. Muneeswaran, G. Priyanka
2013 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES)
(2013)
Proceedings Paper
Computer Science, Theory & Methods
A. Sindhuja, S. Devi Mahalakshmi, K. Vijayalakshmi
2012 IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES (ICACCCT)
(2012)
