FaceDCAPTCHA: Face detection based color image CAPTCHA
01 Feb 2014-Future Generation Computer Systems (Elsevier Science Publishers B. V.)-Vol. 31, Iss: 1, pp 59-68
TL;DR: The proposed algorithm generates a face image-based CAPTCHA that offers better human accuracy and lower machine attack rates compared to existing approaches.
About: This article is published in Future Generation Computer Systems.The article was published on 2014-02-01. It has received 64 citations till now. The article focuses on the topics: CAPTCHA & Face detection.
Citations
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21 Mar 2016
TL;DR: A comprehensive study of reCaptcha is conducted, and a novel low-cost attack that leverages deep learning technologies for the semantic annotation of images is designed, which is extremely effective and applies to the Facebook image captcha.
Abstract: Since their inception, captchas have been widely used for preventing fraudsters from performing illicit actions. Nevertheless, economic incentives have resulted in an arms race, where fraudsters develop automated solvers and, in turn, captcha services tweak their design to break the solvers. Recent work, however, presented a generic attack that can be applied to any text-based captcha scheme. Fittingly, Google recently unveiled the latest version of reCaptcha. The goal of their new system is twofold, to minimize the effort for legitimate users, while requiring tasks that are more challenging to computers than text recognition. ReCaptcha is driven by an "advanced risk analysis system" that evaluates requests and selects the difficulty of the captcha that will be returned. Users may be required to click in a checkbox, or solve a challenge by identifying images with similar content. In this paper, we conduct a comprehensive study of reCaptcha, and explore how the risk analysis process is influenced by each aspect of the request. Through extensive experimentation, we identify flaws that allow adversaries to effortlessly influence the risk analysis, bypass restrictions, and deploy large-scale attacks. Subsequently, we design a novel low-cost attack that leverages deep learning technologies for the semantic annotation of images. Our system is extremely effective, automatically solving 70.78% of the image reCaptcha challenges, while requiring only 19 seconds per challenge. We also apply our attack to the Facebook image captcha and achieve an accuracy of 83.5%. Based on our experimental findings, we propose a series of safeguards and modifications for impacting the scalability and accuracy of our attacks. Overall, while our study focuses on reCaptcha, our findings have wide implications, as the semantic information conveyed via images is increasingly within the realm of automated reasoning, the future of captchas relies on the exploration of novel directions.
119 citations
Cites background from "FaceDCAPTCHA: Face detection based ..."
...Goswami et al. proposed FaceDcaptcha [39], a scheme where users are required to differentiate between actual images of human faces and animated versions of human faces....
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...proposed FaceDcaptcha [39], a scheme where users are required to differentiate between actual images of human faces and animated versions of human faces....
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TL;DR: A novel image-based Captcha named Style Area Captcha (SACaptcha) is proposed in this paper, which is based on semantic information understanding, pixel-level segmentation, and deep learning techniques and it is hoped that this proposal shows promise in the development of image- based Captchas usingDeep learning techniques.
Abstract: The ability of hackers to infiltrate computer systems using computer attack programs and bots led to the development of Captchas or Completely Automated Public Turing Tests to Tell Computers and Humans Apart. The text Captcha is the most popular Captcha scheme given its ease of construction and user friendliness. However, the next generation of hackers and programmers has decreased the expected security of these mechanisms, leaving websites open to attack. Text Captchas are still widely used, because it is believed that the attack speeds are slow, typically two to five seconds per image, and this is not seen as a critical threat. In this paper, we introduce a simple, generic, and fast attack on text Captchas that effectively challenges that supposition. With deep learning techniques, our attack demonstrates a high success rate in breaking the Roman-character-based text Captchas deployed by the top 50 most popular international websites and three Chinese Captchas that use a larger character set. These targeted schemes cover almost all existing resistance mechanisms, demonstrating that our attack techniques are also applicable to other existing Captchas. Does this work then spell the beginning of the end for text-based Captcha? We believe so. A novel image-based Captcha named Style Area Captcha (SACaptcha) is proposed in this paper, which is based on semantic information understanding, pixel-level segmentation, and deep learning techniques. Having demonstrated that text Captchas are no longer secure, we hope that our proposal shows promise in the development of image-based Captchas using deep learning techniques.
71 citations
Additional excerpts
...[37], [38] proposed FR-CAPTCHA and FaceDCAPTCHA....
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TL;DR: The obtained very satisfactory results confirm that the proposed approach may be used for development of new security mechanisms to protect users against cyber-criminal activities and Internet threats.
59 citations
TL;DR: A low-cost system for monitoring of pet animals (dogs) based on their primary animal biometric identifiers using the one-shot similarity and distance metric based learning methods for matching and classifying the extracted features of face images for recognition of pet animal (dog).
53 citations
06 May 2012
TL;DR: The proposed Avatar CAPTCHA asks users to identify avatar faces from a set of 12 grayscale images comprised of a mix of human and avatar faces to be secure against computer programs (bots).
Abstract: This paper introduces Avatar CAPTCHA, an image based approach to distinguish human users from computer programs (bots). The proposed CAPTCHA asks users to identify avatar faces from a set of 12 grayscale images comprised of a mix of human and avatar faces. Experimental results indicate that it can be solved 62% of the time by human users with an average success time of 24 seconds and a positive user rating of 90%. It is designed to be secure against computer programs (bots). Using brute force attack the success rate for a bot to solve it is 1/4096.
46 citations
Cites background from "FaceDCAPTCHA: Face detection based ..."
...(a) ESP [52] (b) ASIRRA [53] (c) What’s Up [54] (d) FaceD [58] (e) Cartoon [66] (f) Jigsaw puzzle [67]....
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...CAPTCHA challenges are also designed using human faces [56], matching distorted faces of several different human subjects [6], finding human face image pairs [57], detection of visually distorted human faces embedded in a complex background [58], distinguishing between human and avatar faces [59] as well as identifying gender of face images [60]....
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References
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01 Oct 2008
TL;DR: The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life, and exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background.
Abstract: Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits “natural” variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.
5,742 citations
"FaceDCAPTCHA: Face detection based ..." refers methods in this paper
...For experimental evaluation,wehave selected about 1800 face images from the LFW face database [29] and 300 cartoon images (from photobucket....
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01 Apr 2004
TL;DR: This paper presents two taxonomies for classifying attacks and defenses in distributed denial-of-service (DDoS) and provides researchers with a better understanding of the problem and the current solution space.
Abstract: Distributed denial-of-service (DDoS) is a rapidly growing problem. The multitude and variety of both the attacks and the defense approaches is overwhelming. This paper presents two taxonomies for classifying attacks and defenses, and thus provides researchers with a better understanding of the problem and the current solution space. The attack classification criteria was selected to highlight commonalities and important features of attack strategies, that define challenges and dictate the design of countermeasures. The defense taxonomy classifies the body of existing DDoS defenses based on their design decisions; it then shows how these decisions dictate the advantages and deficiencies of proposed solutions.
1,866 citations
"FaceDCAPTCHA: Face detection based ..." refers background in this paper
...For instance, scripts or bots can put a heavy load on the servers and enforce aDoS attack, generatemultiple fake accounts (in case of registration forms) which are not profitable to both the service provider and the client [2]....
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Book•
01 Jan 2014
TL;DR: Digital image processing 3rd edition free ebooks download, ece 643 digital image processing i chapter 5, gonzfm i xxii 5 1.
Abstract: amazon com digital image processing 3rd edition, digital image processing 3rd edition pdf, digital image processing 3rd edition 9780131687288, een iust ac ir, download digital image processing 3rd edition pdf ebook, digital image processing gonzalez ebay, digital image processing 3rd edition, digital image processing 3rd edition free ebooks download, ece 643 digital image processing i chapter 5, gonzfm i xxii 5 1
1,830 citations
"FaceDCAPTCHA: Face detection based ..." refers methods in this paper
...– Rotate operation [28] is used to rotate the constituent face and fake images with θ0 angle (Fig....
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...– Blending operation [28] is used to smoothly blend the constituent face and fake images with the background, as shown in Fig....
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TL;DR: This research explored whether human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize.
Abstract: CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words.
1,155 citations
18 Jun 2003
TL;DR: Efficient methods based on shape context matching are developed that can identify the word in an EZ-Gimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time.
Abstract: In this paper we explore object recognition in clutter. We test our object recognition techniques on Gimpy and EZ-Gimpy, examples of visual CAPTCHAs. A CAPTCHA ("Completely Automated Public Turing test to Tell Computers and Humans Apart") is a program that can generate and grade tests that most humans can pass, yet current computer programs can't pass. EZ-Gimpy, currently used by Yahoo, and Gimpy are CAPTCHAs based on word recognition in the presence of clutter. These CAPTCHAs provide excellent test sets since the clutter they contain is adversarial; it is designed to confuse computer programs. We have developed efficient methods based on shape context matching that can identify the word in an EZ-Gimpy image with a success rate of 92%, and the requisite 3 words in a Gimpy image 33% of the time. The problem of identifying words in such severe clutter provides valuable insight into the more general problem of object recognition in scenes. The methods that we present are instances of a framework designed to tackle this general problem.
681 citations
"FaceDCAPTCHA: Face detection based ..." refers background in this paper
...However,Mori andMalik showed that it can be broken and an attack rate of 92% was achieved against EZ-GIMPY [5],...
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...However,Mori andMalik showed that it can be broken and an attack rate of 92% was achieved against EZ-GIMPY [5], ∗ Corresponding author....
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