Wavelet-based color modification detection based on variance ratio

Abstract Color modification is one of the popular image forgery techniques. It can be used to eliminate criminal evidence in various ways, such as modifying the color of a car used in a crime. If the color of a digital image is modified, the locations of the interpolated and original samples may be changed. Because the original and interpolated pixels have different statistical characteristics, these differences can serve as a basic clue for estimating the degree of color modification. It is assumed that the variance of original samples is greater than that of the interpolated samples. Therefore, we present a novel algorithm for color modification estimation using the variance ratio of color difference images in the wavelet domain. The color difference model is used to emphasize the differences between the original and interpolated samples. For color difference images, we execute a wavelet transform and use the highest frequency subband to calculate variances. We define a variance ratio measurement to quantify the level of color modification. Additionally, changed color local regions can be efficiently detected using the proposed algorithm. Experimental results demonstrate that the proposed method generates accurate estimation results for detecting color modification. Compared to the conventional method, our method provides superior color modification detection performance

Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual

Image and video manipulation has been actively used in recent years with the development of multimedia editing technologies. However, object-based video tampering, which adds or removes objects within a video frame, is posing challenges because it is difficult to verify the authenticity of videos. In this paper, we present a novel object-based frame identification network. The proposed method uses symmetrically overlapped motion residuals to enhance the discernment of video frames. Since the proposed motion residual features are generated on the basis of overlapped temporal windows, temporal variations in the video sequence can be exploited in the deep neural network. In addition, this paper introduces an asymmetric network structure for training and testing a single basic convolutional neural network. In the training process, two networks with an identical structure are used, each of which has a different input pair. In the testing step, two types of testing methods corresponding to two- and three-class frame identifications are proposed. We compare the identification accuracy of the proposed method with that of the existing methods. The experimental results demonstrate that the proposed method generates reasonable identification results for both two- and three-class forged frame identifications

Sand-Dust Image Enhancement Using Chromatic Variance Consistency and Gamma Correction-Based Dehazing

In sand-dust environments, the low quality of images captured outdoors adversely affects many remote-based image processing and computer vision systems, because of severe color casts, low contrast, and poor visibility of sand-dust images. In such cases, conventional color correction methods do not guarantee appropriate performance in outdoor computer vision applications. In this paper, we present a novel color correction and dehazing algorithm for sand-dust image enhancement. First, we propose an effective color correction method that preserves the consistency of the chromatic variances and maintains the coincidence of the chromatic means. Next, a transmission map for image dehazing is estimated using the gamma correction for the enhancement of color-corrected sand-dust images. Finally, a cross-correlation-based chromatic histogram shift algorithm is proposed to reduce the reddish artifacts in the enhanced images. We performed extensive experiments for various sand-dust images and compared the performance of the proposed method to that of several existing state-of-the-art enhancement methods. The simulation results indicated that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities

Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual

Image and video manipulation has been actively used in recent years with the development of multimedia editing technologies. However, object-based video tampering, which adds or removes objects within a video frame, is posing challenges because it is difficult to verify the authenticity of videos. In this paper, we present a novel object-based frame identification network. The proposed method uses symmetrically overlapped motion residuals to enhance the discernment of video frames. Since the proposed motion residual features are generated on the basis of overlapped temporal windows, temporal variations in the video sequence can be exploited in the deep neural network. In addition, this paper introduces an asymmetric network structure for training and testing a single basic convolutional neural network. In the training process, two networks with an identical structure are used, each of which has a different input pair. In the testing step, two types of testing methods corresponding to two- and three-class frame identifications are proposed. We compare the identification accuracy of the proposed method with that of the existing methods. The experimental results demonstrate that the proposed method generates reasonable identification results for both two- and three-class forged frame identifications

Underwater Image Enhancement Using Successive Color Correction and Superpixel Dark Channel Prior

Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality

Dong Hun Woo Samsung Electronics

Deinterlacing based on modularization by local frequency characteristic

Sasang typology classification with data reduction and SOM algorithm

Abstract-Personalized medicine has been the major interest for the Western and traditional medicine for a long time since Hippocrates and Yellow Emperor. The purpose of this study was to utilize data reduction and artificial network algorithm for the classification of Sasang types with clinical data. The results showed acceptable type-specific sensitivity and specificity considering its limitation with data. Multidisciplinary approach for the personalized medicine is needed for upcoming integrative medicine