Automatic identification of fictional characters is one of the primary analysis techniques for video content. A common approach to detect characters in live-action movies involves detecting human faces; however, this approach cannot be used in non-realistic domains, such as animated movies. Detection of characters in animated movies presents two major challenges: the same subject of character can be expressed in various unique styles, and there are no stylistic or other restrictions on the nature and design of character objects. To address these challenges, we introduce the “animation adaptive region-based convolutional neural network” model to detect characters in animated movies and determine whether the detected characters are human or non-human types. Our model extends the Faster R-CNN model, which is a two-stage object detector, in the following manner: 1) we add a hierarchical animation adaptation module to learn the variety of unique styles from animated movies using a single model; 2) we incorporate a double-detector architecture to focus on the regions that are visually important in determining the character class. We build a new dataset for the animated character detection task. Experiments on this dataset show that our model outperforms other existing representative object detector models in terms of character detection. Furthermore, our model achieves significant performance improvements compared with previous state-of-the-art methods used for the character dictionary generation task. Our model is robust for a variety of animation styles and can find common visual representations of all types of characters, providing an effective way to detect animated characters.

Estimating printing source is applicable in many forensic situations. In this paper, we propose an electrophotographic printer identification scheme from its printed material, in which imperceptible halftone patterns are contained inherently. The halftone textures in each channel of CMYK domain are analyzed. We construct a histogram from angle values of linear features extracted by Hough transform. By averaging the histograms from multiple images, a printer's reference pattern is identified. The soure printer is determined by a maximum correlation value between the reference patterns and the histogram of given image. Experiments are performed on 9,000 images made by 9 printers. The result supports that the presented scheme clearly recognizes different halftone textures.

Color laser printers are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. First, 39 noise features of color printed images are extracted from the statistical analysis of the HH sub-band on discrete wavelet transform. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, 9 models of 4 brands, Xerox, Konica, HP, Canon, are tested to classify the brand of color laser printer, the color toner, and the model of color laser printer. The results prove that the presented identification method performs well using the noise features of color printed images.

This paper presents a robust watermarking method for curves that uses informed-detection. To embed watermarks, the presented algorithm parameterizes a curve using the B-spline model and acquires the control points of the B-spline model. For these control points, 2D mesh are created by applying Delaunay triangulation and then the mesh spectral analysis is performed to calculate the mesh spectral coefficients where watermark messages are embedded in a spread spectrum way. The watermarked coefficients are inversely transformed to the coordinates of the control points and the watermarked curve is reconstructed by calculating B-spline model with the control points. To detect the embedded watermark, we calculate the difference between the original and watermarked mesh spectral coefficients with the same process for embedding. By calculating correlation coefficients between the detected and candidate watermark, we decide which watermark was embedded.

A blind watermarking algorithm for vector graphic images is presented. The algorithm is resilient to both global and local geometrical distortions. The polygonal line is represented by the wavelet descriptor. An additive watermarking scheme is used to embed the watermark by slightly modifying the wavelet descriptor, and that causes invisible distortions to the coordinates of the vertices. The invariant properties of the wavelet descriptor ensure that the presented algorithm is resilient against both global and local geometrical distortions. Using vector graphic images from contour maps, we demonstrate that the presented algorithm outperforms the algorithm based on the Fourier descriptor.