Han-Ul Jang

A robust 3D mesh watermarking scheme against cropping

Various watermarking schemes achieved the robustness against the usual operations such as simplification, remeshing and noise addition. However, the techniques were not robust against cropping, nevertheless the cropping attack is commonly performed by general editing. In this paper, we propose a robust 3D mesh watermarking method against cropping. We achieve the blind watermarking scheme involving consistent segmentation and the scheme improves the robustness against cropping. The experimental results show that the proposed method achieves higher performance than the previous 3D mesh watermarking methods.

Cropping-resilient 3D mesh watermarking based on consistent segmentation and mesh steganalysis

This paper presents a new approach to 3D mesh watermarking using consistent segmentation and mesh steganalysis. The method is blind, statistical, and highly robust to cropping attack. The primary watermarking domain is calculated by shape diameter function and the outliers of segments are eliminated by computing the consistency interval of vertex norms. In the watermark embedding process, the mesh is divided into several segments and the same watermark is inserted in each segment. In the watermark extraction process, the final watermark among watermark candidates extracted from multiple segments is determined through watermark trace analysis that is kind of mesh steganalysis. We analyze the watermark trace energy of multiple segments of a mesh and detect the final watermark in the segment with the highest watermark trace energy. To analyze the watermark trace energy, we employ nonlinear least-squares fitting. The experimental results show that the proposed method not only achieves significantly high robustness against cropping attack, but also resists common signal processing attacks such as additive noise, quantization, smoothing and simplification.

DeepPore: Fingerprint Pore Extraction Using Deep Convolutional Neural Networks

As technological developments have enabled high-quality fingerprint scanning, sweat pores, one of the Level 3 features of fingerprints, have been successfully used in automatic fingerprint recognition systems (AFRS). Since the pore extraction process is a critical step for AFRS, high accuracy is required. However, it is difficult to extract the pore correctly because the pore shape depends on the person, region, and pore type. To solve the problem, we have presented a pore extraction method using deep convolutional neural networks and pore intensity refinement. The deep networks are used to detect pores in detail using a large area of a fingerprint image. We then refine the pore information by finding local maxima to identify pores with different intensities in the fingerprint image. The experimental results show that our pore extraction method performs better than the state-of-the-art methods.

Hue modification estimation using sensor pattern noise

In digital image forensics, previous methods for hue forgery detection cannot be used after common image processing such as resizing and JPEG compression. In this paper, we suggest a robust forensics scheme for estimating hue modification of images. To achieve this goal, we use sensor pattern noise from each color channel of un-tampered images as the ground truth. Since we know the unique characteristics of each color channel, we can estimate a hue modification by testing suspicious images for all hue changes. The results confirms that the proposed method distinguishes hue modification and estimates the changed degree; moreover, it provides robustness against resizing and JPEG compression.

Detecting composite image manipulation based on deep neural networks

In this paper, we propose a composite manipulation detection method based on convolutional neural networks (CNNs). To our best knowledge, this is the first work applying deep learning for composite forgery detection. The proposed technique defines three types of attacks that occurred frequently during image forging and detects when they are concurrently applied to images. To do this, we learn the statistical change due to the manipulation through the proposed CNN architecture and classify the manipulated image. The proposed technique is effective since it learns integrated image of composite manipulation and extracts characteristic distinguished from original image. Since most attacks are applied in a composite way in real environment, the approach of the proposed technique has practical advantages compared to traditional forensics scheme. In addition, the experimental results demonstrate the reliability of the proposed method through results of high performance.

Improved 3D Mesh Steganalysis Using Homogeneous Kernel Map

Steganalysis targets to detect the existence of hidden information in a given content. In this paper we propose to use a local feature set which is designed to enhance discrimination of features obtained from a cover and a stego mesh. The proposed feature captures the fine deformation of the 3D mesh surface induced by a steganography or watermarking method. In our 3D steganalysis approach, in addition, we apply the homogeneous kernel map to the local feature set, which make it possible to bring much more discrimination via non-linear mapping. The proposed feature set and its combination with the homogeneous feature map have shown good performance on two different steganography and watermarking algorithm with a well known and widely used 3D mesh database through repeated experiments.

Fingerprint Spoof Detection Using Contrast Enhancement and Convolutional Neural Networks

Recently, as biometric technology grows rapidly, the importance of fingerprint spoof detection technique is emerging. In this paper, we propose a technique to detect forged fingerprints using contrast enhancement and Convolutional Neural Networks (CNNs). The proposed method detects the fingerprint spoof by performing contrast enhancement to improve the recognition rate of the fingerprint image, judging whether the sub-block of fingerprint image is falsified through CNNs composed of 6 weight layers and totalizing the result. Our fingerprint spoof detector has a high accuracy of 99.8% on average and has high accuracy even after experimenting with one detector in all datasets.

Robust Imperceptible Video Watermarking for MPEG Compression and DA-AD Conversion Using Visual Masking

In this paper, we propose a robust and invisible video watermarking scheme. To ensure robustness against various non-hostile disturbances which can occur during the distribution of digital content, the proposed system selects certain blocks using a robust and imperceptible block selection scheme and watermarks are embedded into these blocks using spread-spectrum watermarking in DCT domain. In addition, visual masking is applied to the watermarking embedding process for high invisibility. Our system is designed to extract 16 bits data in any 15-second interval. Experimental results show that the proposed system offers high invisibility and that it is robust against MPEG-4 compression and DA-AD conversion.

Exposing Digital Forgeries by Detecting a Contextual Violation Using Deep Neural Networks

Previous digital image forensics focused on the low-level features that include traces of the image modifying history. In this paper, we present a framework to detect the manipulation of images through a contextual violation. First, we proposed a context learning convolutional neural networks (CL-CNN) that detects the contextual violation in the image. In combination with a well-known object detector such as R-CNN, the proposed method can evaluate the contextual scores according to the combination of objects in the image. Through experiments, we showed that our method effectively detects the contextual violation in the target image.

Perceptual 3D Watermarking Using Mesh Saliency

In this paper, we introduce a novel blind 3D mesh watermarking method which focuses on preserving the appearance of the watermarked model. Despite the high transparency achieved by existing 3D watermarking schemes, we observe that only a small amount of geometric error can bring a significant impact to appearance of 3D models, especially in visually important regions. We integrate this human perceptual importance, called saliency, to control the distortions on surfaces. Our method enhances the imperceptibility while maintaining the efficiency of processing spatial information by conjugating spatial and spectral regions. We use the vertex norm distribution and solve the quadratic error minimization problem to insert watermark bits. Experimental results demonstrate that our method performs well for perceived visual quality and also robustness against various geometric attacks.