3,179 research outputs found
Computer Graphic and Photographic Image Classification using Local Image Descriptors
With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy
How Realistic is Photorealistic?
From its inception in 1960, computer graphics (CG) technology has quickly progressed from simple 3-D models to complex, photorealistic recreations of the human face and body. Alongside this innovation, lawmakers and courts in the United States have struggled to define what is illegal, what is obscene”, and what is protected under the First Amendment with regards to child pornography. What has emerged from this debate is that the laws surrounding child pornography hinge on whether the material in question is photographic or CG. To this end, we measure how reliable the human visual system is in distinguishing CG from photographic images. After establishing a baseline for observer performance in this task as a function of both resolution and contrast, we address the following two questions: (1) is it possible to improve observer performance by isolating select features of the face? and (2) will training observers improve their performance
A Robust Approach Towards Distinguishing Natural and Computer Generated Images using Multi-Colorspace fused and Enriched Vision Transformer
The works in literature classifying natural and computer generated images are
mostly designed as binary tasks either considering natural images versus
computer graphics images only or natural images versus GAN generated images
only, but not natural images versus both classes of the generated images. Also,
even though this forensic classification task of distinguishing natural and
computer generated images gets the support of the new convolutional neural
networks and transformer based architectures that can give remarkable
classification accuracies, they are seen to fail over the images that have
undergone some post-processing operations usually performed to deceive the
forensic algorithms, such as JPEG compression, gaussian noise, etc. This work
proposes a robust approach towards distinguishing natural and computer
generated images including both, computer graphics and GAN generated images
using a fusion of two vision transformers where each of the transformer
networks operates in different color spaces, one in RGB and the other in YCbCr
color space. The proposed approach achieves high performance gain when compared
to a set of baselines, and also achieves higher robustness and generalizability
than the baselines. The features of the proposed model when visualized are seen
to obtain higher separability for the classes than the input image features and
the baseline features. This work also studies the attention map visualizations
of the networks of the fused model and observes that the proposed methodology
can capture more image information relevant to the forensic task of classifying
natural and generated images
Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
The problem of distinguishing natural images from photo-realistic
computer-generated ones either addresses natural images versus computer
graphics or natural images versus GAN images, at a time. But in a real-world
image forensic scenario, it is highly essential to consider all categories of
image generation, since in most cases image generation is unknown. We, for the
first time, to our best knowledge, approach the problem of distinguishing
natural images from photo-realistic computer-generated images as a three-class
classification task classifying natural, computer graphics, and GAN images. For
the task, we propose a Multi-Colorspace fused EfficientNet model by parallelly
fusing three EfficientNet networks that follow transfer learning methodology
where each network operates in different colorspaces, RGB, LCH, and HSV, chosen
after analyzing the efficacy of various colorspace transformations in this
image forensics problem. Our model outperforms the baselines in terms of
accuracy, robustness towards post-processing, and generalizability towards
other datasets. We conduct psychophysics experiments to understand how
accurately humans can distinguish natural, computer graphics, and GAN images
where we could observe that humans find difficulty in classifying these images,
particularly the computer-generated images, indicating the necessity of
computational algorithms for the task. We also analyze the behavior of our
model through visual explanations to understand salient regions that contribute
to the model's decision making and compare with manual explanations provided by
human participants in the form of region markings, where we could observe
similarities in both the explanations indicating the powerful nature of our
model to take the decisions meaningfully.Comment: 13 page
Review on passive approaches for detecting image tampering
This paper defines the presently used methods and approaches in the domain of digital image forgery detection. A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Passive, or blind, approaches for detecting image tampering are regarded as a new direction of research. In recent years, there has been significant work performed in this highly active area of research. Passive approaches do not depend on hidden data to detect image forgeries, but only utilize the statistics and/or content of the image in question to verify its genuineness. The specific types of forgery detection techniques are discussed below
Image statistical frameworks for digital image forensics
The advances of digital cameras, scanners, printers, image editing tools, smartphones, tablet personal computers as well as high-speed networks have made a digital image a conventional medium for visual information. Creation, duplication, distribution, or tampering of such a medium can be easily done, which calls for the necessity to be able to trace back the authenticity or history of the medium. Digital image forensics is an emerging research area that aims to resolve the imposed problem and has grown in popularity over the past decade. On the other hand, anti-forensics has emerged over the past few years as a relatively new branch of research, aiming at revealing the weakness of the forensic technology.
These two sides of research move digital image forensic technologies to the next higher level. Three major contributions are presented in this dissertation as follows.
First, an effective multi-resolution image statistical framework for digital image forensics of passive-blind nature is presented in the frequency domain. The image statistical framework is generated by applying Markovian rake transform to image luminance component. Markovian rake transform is the applications of Markov process to difference arrays which are derived from the quantized block discrete cosine transform 2-D arrays with multiple block sizes. The efficacy and universality of the framework is then evaluated in two major applications of digital image forensics: 1) digital image tampering detection; 2) classification of computer graphics and photographic images.
Second, a simple yet effective anti-forensic scheme is proposed, capable of obfuscating double JPEG compression artifacts, which may vital information for image forensics, for instance, digital image tampering detection. Shrink-and-zoom (SAZ) attack, the proposed scheme, is simply based on image resizing and bilinear interpolation. The effectiveness of SAZ has been evaluated over two promising double JPEG compression schemes and the outcome reveals that the proposed scheme is effective, especially in the cases that the first quality factor is lower than the second quality factor.
Third, an advanced textural image statistical framework in the spatial domain is proposed, utilizing local binary pattern (LBP) schemes to model local image statistics on various kinds of residual images including higher-order ones. The proposed framework can be implemented either in single- or multi-resolution setting depending on the nature of application of interest. The efficacy of the proposed framework is evaluated on two forensic applications: 1) steganalysis with emphasis on HUGO (Highly Undetectable Steganography), an advanced steganographic scheme embedding hidden data in a content-adaptive manner locally into some image regions which are difficult for modeling image statics; 2) image recapture detection (IRD). The outcomes of the evaluations suggest that the proposed framework is effective, not only for detecting local changes which is in line with the nature of HUGO, but also for detecting global difference (the nature of IRD)
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