Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis

Peng, Fei; Dongdong, Zhou; Long, Min; Sun, Xingming

doi:10.1016/j.aeue.2016.11.009

Cited by 72 publications

(38 citation statements)

References 23 publications

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“…However, since 3D face models are created from scratch with high fidelity texture data, these methods could not detect any forgery on spoofing media. On the other hand, new approaches (such as discrepancy analysis on color filter array of camera sensor noise or multi-fractal and regression analysis on discriminating natural and computer generated images) could be used as countermeasures against 3D-face-model-based attacks [33], [34]. However, attackers can extract genuine noise patterns or features from existing or captured images to embed them into generated video in a compromised device, thus, these defense mechanisms also fail against our threat model [35].…”

Section: B Compromising Attacks and Defensesmentioning

confidence: 99%

rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System

Uzun¹

2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-Facial/voice-based authentication is becoming increasingly popular (e.g., already adopted by MasterCard and AliPay), because it is easy to use. In particular, users can now authenticate themselves to online services by using their mobile phone to show themselves performing simple tasks like blinking or smiling in front of its built-in camera. Our study shows that many of the publicly available facial/voice recognition services (e.g. Microsoft Cognitive Services or Amazon Rekognition) are vulnerable to even the most primitive attacks. Furthermore, recent work on modeling a person's face/voice (e.g. Face2Face [1]) allows an adversary to create very authentic video/audio of any target victim to impersonate that target. All it takes to launch such attacks are a few pictures and voice samples of a victim, which can all be obtained by either abusing the camera and microphone of the victim's phone, or through the victim's social media account. In this work, we propose the Real Time Captcha (rtCaptcha) system, which stops/slows down such an attack by turning the adversary's task from creating authentic video/audio of the target victim performing known authentication tasks (e.g., smile, blink) to figuring out what is the authentication task, which is encoded as a Captcha. Specifically, when a user tries to authenticate using rtCaptcha, they will be presented a Captcha and will be asked to take a "selfie" video while announcing the answer to the Captcha. As such, the security guarantee of our system comes from the strength of Captcha, and not how well we can distinguish real faces/voices from synthesized ones. To demonstrate the usability and security of rtCaptcha, we conducted a user study to measure human response times to the most popular Captcha schemes. Our experiments show that, thanks to the humans' speed of solving Captchas, adversaries will have to solve Captchas in less than 2 seconds in order to appear live/human and defeat rtCaptcha, which is not possible for the best settings on the attack side.

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Section: B Compromising Attacks and Defensesmentioning

confidence: 99%

rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System

Uzun¹

2018

Proceedings 2018 Network and Distributed System Security Symposium

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“…However, these images can be described by a continuous spectrum with different exponents in different scales. This property is usually called multi-fractal property (Ne and Parga, 2000;Peng et al, 2017). If we assume multi-fractal properties of texture features on the solar images do not change between images observed in the same wavelength, with one high resolution image as reference, we can easily discriminate images with different blur level.…”

Section: Principle Of the Perception Evaluationmentioning

confidence: 99%

Perception Evaluation: A New Solar Image Quality Metric Based on the Multi-fractal Property of Texture Features

et al. 2019

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Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques. Based on the assumption that texture features in solar images are multi-fractal which can be extracted by a trained deep neural network as feature maps, a new reduced-reference objective image quality metric, the perception evaluation is proposed. The perception evaluation is defined as cosine distance of Gram matrix between feature maps extracted from high resolution reference images and that from blurred images. We evaluate performance of the perception evaluation with simulated blurred images and real observation images. The results show that with a high resolution image as reference, the perception evaluation can give robust estimate of image quality for solar images of different scenes.

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“…Detection rate Before After Before After Wu et al [2] 64.65 82.73 35.51 71.66 Peng et al [3] 50.20 0.20 100.00 0.00 Nguyen et al [4] 32.31 41.27 0.49 18.17…”

Section: Spoofing Detectors Accuracymentioning

confidence: 99%

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

Nguyen

Tieu

Nguyen-Son

et al. 2018

2018 IEEE International Conference on Multimedia and Expo (ICME)

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Making computer-generated (CG) images more difficult to detect is an interesting problem in computer graphics and security. While most approaches focus on the image rendering phase, this paper presents a method based on increasing the naturalness of CG facial images from the perspective of spoofing detectors. The proposed method is implemented using a convolutional neural network (CNN) comprising two autoencoders and a transformer and is trained using a black-box discriminator without gradient information. Over 50% of the transformed CG images were not detected by three state-of-the-art spoofing detectors. This capability raises an alarm regarding the reliability of facial authentication systems, which are becoming widely used in daily life.

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Discrimination of natural images and computer generated graphics based on multi-fractal and regression analysis

Cited by 72 publications

References 23 publications

rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System

rtCaptcha: A Real-Time CAPTCHA Based Liveness Detection System

Perception Evaluation: A New Solar Image Quality Metric Based on the Multi-fractal Property of Texture Features

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

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