• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.671-685
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• 2023

By applying artificial intelligence to image editing technology, it has become possible to generate high-quality images with minimal traces of manipulation. However, since these technologies can be misused for criminal activities such as dissemination of false information, destruction of evidence, and denial of facts, it is crucial to implement strong countermeasures. In this study, image file and mobile forensic artifacts analysis were conducted for detecting image manipulation. Image file analysis involves parsing the metadata of manipulated images and comparing them with a Reference DB to detect manipulation. The Reference DB is a database that collects manipulation-related traces left in image metadata, which serves as a criterion for detecting image manipulation. In the mobile forensic artifacts analysis, packages related to image editing tools were extracted and analyzed to aid the detection of image manipulation. The proposed methodology overcomes the limitations of existing graphic feature-based analysis and combines with image processing techniques, providing the advantage of reducing false positives. The research results demonstrate the significant role of such methodology in digital forensic investigation and analysis. Additionally, We provide the code for parsing image metadata and the Reference DB along with the dataset of manipulated images, aiming to contribute to related research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1671-1686
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• 2022

Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.67-73
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• 2019

In the current era of digital technology, and with the help of existing software, digital photo manipulation is becoming easier and faster. One example of this is the development of powerful image processing software that makes it easy for a digital image to be manipulated and edited. It is therefore very important to protect and maintain public trust in digital images. Several methods have been developed to detect image manipulation. In this paper, we compare two methods for detecting image duplication due to copy-move actions, namely the polar coordinate system and the histogram of oriented gradients methods. The former is a method based on the transfer of a Cartesian image to a polar form, making it easy to tell whether there are objects that have undergone a copy/move in an image, while the latter is a method for retrieving information related to the distribution, which uses a target in the local area as a tool to represent the shape of the target. We compare the accuracy, speed and memory usage of these two methods.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.25 no.1
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• pp.76-83
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• 2012

In a real-time indoor place recognition system using image features detection, specific markers included in input image should be detected exactly and quickly. However because the same markers in image are shown up differently depending to movement, direction and angle of camera, it is required a method to solve such problems. This paper proposes a technique to extract the features of object without regard to change of the object scale. To support real-time operation, it adopts SURF(Speeded up Robust Features) which enables fast feature detection. Another feature of this system is the user mark designation which makes possible for user to designate marks from input image for location detection in advance. Unlike to use hardware marks, the feature above has an advantage that the designated marks can be used without any manipulation to recognize location in input image.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.1093-1096
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• 2002

In this paper, a new semi-automated cyber character generating method is presented. Local edge detection tools extract face contour from graphic image files. Some graphic manipulation process detailed touch to obtain neat face contour. This method shortens the making process dramatically while maintaining the good quality similar to real face image. Some of the processed images are illustrated for clear explanation.

• Journal of IKEEE
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• v.22 no.2
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• pp.227-232
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• 2018

Nowadays, image manipulation is enormously popular and easier than ever with tons of convenient images editing tools. After several simple operations, users can get visually attractive images which easily trick viewers. In this paper, we propose a fast algorithm which can detect the image splicing using the Markov features. The proposed algorithm reduces the computational complexity by removing unnecessary Markov features which are not used in the image splicing detection process. The performance of the proposed algorithm is evaluated using a famous image splicing dataset which is publicly available. The experimental results show that the proposed technique outperforms the state-of-the-art splicing detection methods.

• Journal of Internet Computing and Services
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• v.4 no.4
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• pp.27-34
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• 2003

Digital image manipulation software is now readily available on personal computers. It is therefore very simple to tamper with any image and make it available to others. Therefore. copyright protection of digital contents and insurance of digital image integrity become major issues. In this paper, we propose a hybrid watermarking method to identify locations of tampered region as well as copyright. Our proposed algorithms embed the PN-sequence into low frequency sub-band of the wavelet transform domain and it doesn't need the original image in extraction procedure. The experimental results show good robustness against any signal processing with tamper detection on still image.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.203-208
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• 2019

Digital watermarking was developed to protect copyright by discouraging the illegal copying of digital content. On the other hand, recently, watermarking has also been used to verify the integrity of digital content, such as medical images, and detect illegal manipulation or distortion locations. Watermarking should be tenacious so as to protect copyright from illegal copying and should remain firm to the content through a range of attacks, such as distortion or filtering. At the same time, however, it should be removed easily even in a slight transformation of the material to verify the integrity. Therefore, this paper proposes a watermarking technique that easily checks and verifies the deformation or manipulation of digital images. In the proposed method, the entire image was examined in $16{\times}16$ blocks to check for deformation of the image. When deformation was detected, further inspection proceeded in $4{\times}4$ blocks and the location where deformation occurred was identified.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.267-272
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• 2007

This paper proposes a new approach fur the detection of facial feature regions using the characteristic of DCT(discrete cosine transformation) thatconcentrates the energy of an image into lower frequency coefficients. Since the facial features are pertained to relatively high frequency in a face image, the inverse DCT after removing the DCT's coefficients corresponding to the lower frequencies generates the image where the facial feature regions are emphasized. Thus the facial regions can be easily segmented from the inversed image using any differential operator. In the segmented region, facial features can be found using face template. The proposed algorithm has been tested with the image MIT's CBCL DB and the Yale facedatabase B. The experimental results have shown superior performance under the variations of image size and lighting condition.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.299-305
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• 2021

In this study, the task of robotic tidy-up is to clean the current environment up exactly like a target image. To perform a tidy-up task using a robot, it is necessary to estimate the pose of various objects and to classify the objects. Pose estimation requires the CAD model of an object, but these models of most objects in daily life are not available. Therefore, this study proposes an algorithm that uses point cloud and PCA to estimate the pose of objects without the help of CAD models in cluttered environments. In addition, objects are usually detected using a deep learning-based object detection. However, this method has a limitation in that only the learned objects can be recognized, and it may take a long time to learn. This study proposes an image matching based on few-shot learning and Siamese network. It was shown from experiments that the proposed method can be effectively applied to the robotic tidy-up system, which showed a success rate of 85% in the tidy-up task.