Yong Ho Moon

Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion

Abstract. We propose an efficient Markov feature extraction method for color image splicing detection. The maximum value among the various directional difference values in the discrete cosine transform domain of three color channels is used to choose the Markov features. We show that the discriminability for slicing detection is increased through the maximization process from the point of view of the Kullback–Leibler divergence. In addition, we present a threshold expansion and Markov state decomposition algorithm. Threshold expansion reduces the information loss caused by the coefficient thresholding that is used to restrict the number of Markov features. To compensate the increased number of features due to the threshold expansion, we propose an even–odd Markov state decomposition algorithm. A fixed number of features, regardless of the difference directions, color channels and test datasets, are used in the proposed algorithm. We introduce three kinds of Markov feature vectors. The number of Markov features for splicing detection used in this paper is relatively small compared to the conventional methods, and our method does not require additional feature reduction algorithms. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection.

Simplified noise model parameter estimation for signal-dependent noise

In this paper, we present a noise parameter estimation method using a simplified signal-dependent noise model. The generic Poisson-Gaussian noise model is simplified to a Gaussian-Gaussian noise model. From the simplified noise model, we experimentally verify that the value obtained by the robust median estimator is almost the same as the mean of the noise standard deviation. Based on this property, the noise model parameters are estimated by the least square method. Simulation results show that the estimation performance using our proposed algorithm is compatible with the performance of the existing method. Our method can generate good parameter estimation results with reduced computational complexity.

Blind identification of image manipulation type using mixed statistical moments

Abstract. We present a blind identification of image manipulation types such as blurring, scaling, sharpening, and histogram equalization. Motivated by the fact that image manipulations can change the frequency characteristics of an image, we introduce three types of feature vectors composed of statistical moments. The proposed statistical moments are generated from separated wavelet histograms, the characteristic functions of the wavelet variance, and the characteristic functions of the spatial image. Our method can solve the n-class classification problem. Through experimental simulations, we demonstrate that our proposed method can achieve high performance in manipulation type detection. The average rate of the correctly identified manipulation types is as high as 99.22%, using 10,800 test images and six manipulation types including the authentic image.

An improved coeff_token variable length decoding mehod for low power design of H.264/AVC CAVLC decoder

In this paper, we propose an improved coeff_token variable-length decoding (VLD) method in the CAVLC decoder. The number of memory accesses is one of the significant issues in the applications such as DMB and videophone services, where the low-power consumption and high-speed operation are required. In order to cut down the heavy memory accesses, a new fast VLD method is developed through the careful examination of the codewords in variable-length code tables (VLCTs). The simulation results show that the proposed method achieves an approximately 60% memory access saving without video-quality degradation, compared to the existing fast coeff-token VLD method.

Quantization-based Markov feature extraction method for image splicing detection

In this paper, we propose an efficient Markov feature extraction method for image splicing detection using discrete cosine transform coefficient quantization. The quantization operation reduces the information loss caused by the coefficient thresholding used to restrict the number of Markov features. The splicing detection performance is improved because the quantization method enlarges the discrimination of the probability distributions between the authentic and the spliced images. In this paper, we present two Markov feature selection algorithms. After quantization operation, we choose the sum of three directional Markov transition probability values at the corresponding position in the probability matrix as a first feature vector. For the second feature vector, the maximum value among the three directional difference values of the three color channels is used. A fixed number of features, regardless of the color channels and test datasets, are used in the proposed algorithm. Through experimental simulations, we demonstrate that the proposed method achieves high performance in splicing detection. The average detection accuracy is over than 97% on three well-known splicing detection image datasets without the use of additional feature reduction algorithms. Furthermore, we achieve reasonable forgery detection performance for more modern and realistic dataset.

Advanced Data-Transmission Scheme for a High-Performance Mission Computer

DOI: 10.2514/1.I010130 In this paper, an advanced data-transmission scheme is proposed to enhance the performance of a mission computer based on the ARINC-429 avionic data bus standard. The amount of flight data transmitted to the mission computer has continually increased due to the adoption of composite missions, new avionic systems, and advanced functionalities. This makes it difficult to obtain a high-performance mission computer because the mission computer should consume a large portion of the fixed time interval in receiving flight data. To overcome this problem, we developed a new data format and transmission method based on detailed analysis of the characteristics of flight data and the data format of ARINC-429. The proposed scheme was verified by observing its performance on a VxWorksbased test platform. Experimental results show that theproposedscheme achievesa computational time gain of 14% andtransmissionrategainof34%,withoutincreasingthesize,weight,andpower.Inaddition,theproposedschemeis useful for extending the life of the mission computer without changing existing hardware.

Efficient memory architecture for fast total_zeros decoding in H.264/AVC CAVLC decoder

A large amount of memory accesses are required for CAVLC decoding in the H.264/AVC baseline profile. It is an important issue for low-power implementation of mobile video applications because the memory access function results in considerable power consumption. We have found some distinctive features in the variable-length code table (VLCT) of total_zeros element through a careful analysis of the codeword. Based on these features, efficient memory architecture is designed and new decoding method is developed in this paper. The simulation results show that the new decoding with the proposed memory architecture achieves an approximately 80~90% memory access saving compared to conventional decoding.