Patent Application: US-66885307-A

Abstract:
in a system and method for the generation of attack - resistant , user - friendly , image - based captchas , controlled distortions are applied to randomly chosen images and presented to a user for annotation from a given list of words . an image is presented that contains multiple connected but independent images with the borders between them distorted or otherwise visually obfuscated in a way that a computer cannot distinguish the borders and a user selects near the center of one of the images the distortions are performed in a way that satisfies the incongruous requirements of low perceptual degradation and high resistance to attack by content - based image retrieval systems . word choices are carefully generated to avoid ambiguity as well as to avoid attacks based on the choices themselves .

Description:
given a database of images of simple concepts , a two - step user - interface allows quick testing for humans while being expensive for machines . controlled composite distortions on the images maintain visual clarity for recognition by humans while making the same difficult for automated systems . requiring the user to type in the annotation may lead to problems like misspelling and polysemy [ 3 ]. in our system , we present to the user a set of word choices , and the user must choose the most suitable image descriptor . a problem with generating word choices is that we might end up with , for example , the word “ dog ” and the word “ wolf ” in the list , and this may cause ambiguity in labeling . to avoid this problem , we propose a wordnet - based [ 5 ] algorithm to generate a semantically non - overlapping set of word choices while preventing odd - oize - olit attacks using the choices themselves . because the number of choices are limited , the location of the mouse - click on the composite image acts as additional user input , and together with the annotation , it forms the two - step mechanism to reduce the rate of random attacks . a reason for naming our system imagination is that it aims to exploit human imagination power gained through exposure / experience , allowing interpretation of pictures amidst distortion / clutter . the overall system architecture is shown in fig1 . we have a two - round click - and - annotate process in which a user needs to click on the interface 4 times in all . the system presents the user with a set of 8 images tiled to form a single composite image . the user must then select an image she wants to annotate by clicking near its geometric center . if the location of the click is near one of the centers , a controlled distortion is performed on the selected image and displayed along with a set of word choices pertaining to it , and the user must choose the appropriate one . if the click is not near any of the centers or the choice is invalid , the test restarts . otherwise , this click - and - annotate process is repeated one more time , passing which the captcha is considered cleared . the reason for having the click phase is that the word choices are limited , making random attack rate fairly high . instead of having numerous rounds of annotate , user clicks tend to make the system more user - friendly , while decreasing the attack rate . the first step is the composite image generation . given an annotated database of images i consisting of simple concepts and objects , the system randomly selects a set of 8 images { i 1 , . . . , i 8 } with their corresponding annotations { w 1 , . . . , w 8 }. a rectangular region is divided into 8 random orthogonal partitions { p 1 , . . . , p 8 } and by a one - to - one mapping i k → p k , each image is placed into a partition , scaled as necessary , forming a preliminary composite image c . a two - stage dithering using the floyd - steinberg error - diffusion algorithm is then performed . the image c is randomly divided into two different sets of 8 orthogonal partitions { p ′ 1 , . . . , p ′ 8 } and { p ″ 1 , . . . , p ″ 8 }, and dithering is applied on these two sets sequentially , forming the required composite image c ″. dithering parameters that are varied independently over each partition include the base colors used ( 18 , randomly chosen in rgb space ), resulting in different color gamuts , and the coefficients used for spreading the quantization error . the same ratio of coefficients 7 / 16 , 1 / 16 , 5 / 16 and 3 / 16 are used for neighboring pixels , but they are multiplied by a factor ok , which is chosen randomly in the range of 0 . 5 - 1 . 5 . these steps ensure that the task of automatically determining the geometric centers of the images remain challenging , while human imagination continues to steer rough identification . the difficulty in automated detection arises from the fact that partitioning and subsequent dithering cuts the original image tiling arbitrarily , making techniques such as edge / rectangle detection generate many false boundaries ( see example in fig2 for an idea ). let the location of the actual user click be ( x , y ). suppose the corner coordinates of the 8 images within the composite image be { ( x 1 k , y 1 k , x 2 k , y 2 k ) , k = 1 , … ⁢ ⁢ 8 } . min k ⁢ { ( x - x 1 2 + x 2 k 2 ) + ( y - x 1 k + x 2 k 2 ) } ≤ r 2 where tolerance r is a constant determining the radius around the actual geometric centers of each image up to which this validity holds . note that this parameter adjusts the wall between user - friendliness and reliability ( larger tolerance r also means higher random attack rate ). suppose the response is valid and the minimum is achieved for image i k . then a randomly chosen composite distortion from among an allowed distortion set d is performed on ik and displayed in its original size and aspect ratio . based on the corresponding annotation w k , a word choice set w is generated . generation of d and w are described below . images can be distorted in various ways . our design of an allowed distortion set d requires the inclusion of distortions that maintain good visual clarity for recognition by humans while making automated recognition hard . captcha requires that the annotated database and relevant code be publicly available , for added security . if undistorted images from the database were presented as captchas , attacks would be trivial . previous systems proposed [ 3 ] are liable to such attacks . if the images are randomly distorted before being presented to the user [ 1 ], it may still be possible to perform attacks using computer vision techniques such as affine / scale invariant features and cbir . we aim at building image - based captchas secure against such attacks . certain assumptions about possible attack strategies are needed in order to design attack - resistant distortions . here , we assume that the only feasible way is to use cbir ( content - based image retrieval ) to perform inexact matches between the distorted image and the set of images in the database , and use the label associated with an appropriately matched one for attack . this assumption is reasonable since attack strategy needs to work on the entire image database in real - time in order to be effective , and image retrieval usually scales better than other techniques . suppose d ( i k ) indicates the application of distortion d on image i k , and sp ( i j , i k ) denotes the similarity measure between images i j and i k using image retrieval system s p . considering the worst - case scenario where the attacker has access to the database i , the cbir system s p , and the distortion algorithms in d , a good attack strategy can be as follows : the attacker studies the distribution of the distances between ( 1 ) a distorted image and its original , f 1 ( 7 ), and ( 2 ) a distorted image and all other images in i , f 2 ( x ). for a given distorted image d ( i j ), she can then compute sp ( d ( i ), i k ) ∀ i k ε i . if there are significant differences between f 1 ( x ) and f 2 ( x ), the attacker can exploit this to eliminate images in i that are unlikely to be i j . one way to do this is to set a confidence interval [ a , b ] at say 90 % level around the mean of distribution f 1 and then eliminating all images i k except those with a ≦ s p ( di j ), i k )≦ b . with n images contained in i , and a random guess , p ( attack )= n − 1 , while after elimination , p ⁡ ( attack ) = ( 0 . 9 ⁢ ⁢ n ⁢ ∫ a b ⁢ f 2 ⁡ ( x ) ⁢ ⅆ x ) . this idea is illustrated in fig3 . our goal is to counter such attacks by choosing distortions d that minimize p ( attack ), i . e . maximize ∫ a b ⁢ f 2 ⁡ ( x ) ⁢ ⅆ x . although f 2 ( x ) is dependent on d ( i j ), there is no easy way to control f 2 directly through a choice of d . instead , we design d by choosing distortions d that give a value for p ( attack ) below a chosen threshold t . in this way , we ensure that probabilistically , given distorted image d ( i j ) and all data / code , the attacker can identify the original image i , in i ( and hence successfully attack ) with a probability of at most t . we found through experiments that while f 2 ( x ) tends to be a wider distribution , f 1 ( x ) is usually a narrow band with mean closer to the origin , and both are only slightly skewed from gaussian distributions . intuitively , under such circumstances , if δ =| f 1 − f 2 |, p ( attack ) decreases as δ → 0 ( see fig3 ). one underlying assumption for our probabilistic criteria is that distributions f 1 ( x ) and f 2 ( x ) are invariant to the choice of i j . though this does not hold precisely , it does so for a majority of the i j in i , allowing us the liberty to make the assumption to get a significantly simpler criteria . for experiments , our choice of s p is a state - of - the - art similarity measure ( or image distance ), the integrated region matching ( irm ) used in the simplicity system [ 11 ]. while other image comparison methods exist [ 9 ], irm produces relatively fast ( speed of attack is critical here ) and accurate inexact matches . note that the actual features or systems to be used by the attacker is unknown , but for the purpose of launching effective attacks , alternate choices seem unlikely . if there are better ways to attack the system , then these in turn improve the state - of - the - art in retrieving distorted images , and new sets of distortions need to be included in d . we have not considered attacks based on interest points or other such features . our experiments revealed that isolated distortions are insufficient in fooling the retrieval systems . considering attack chances and visual clarity after distortion , we came up with a set of 11 candidate composite distortions { d 1 , . . . , d 11 } along the framework shown in fig4 . each one is composed of a combination of dithering , partitioning , quantization , noise addition , color re - mapping , and selective cut - and - resize . dithering seemed particularly suitable since clarity was retained while low - level feature extraction ( and thus image matching ) was affected . we applied the distortions to 300 corel images and used irm to calculate f 1 ( x ) and f 2 ( x ) for each d k . based on our criteria , a suitable threshold t , and a 90 % confidence interval around f 1 , distortions d 5 , d 8 , d 9 and d 11 were chosen as part of the allowed distortion set d . note that we define here a formal procedure for choosing composite distortions , and select 4 acceptable ones out of a set of 11 ad - hoc distortions . details of these distortions are not critical to the invention , since other distortions can be added to d by this procedure . for word choice generation , factors related to image - based captchas that have not been previously addressed are ( 1 ) it may be possible to remove ambiguity in labeling images ( hence making annotation easier for humans ) by the choices themselves , ( 2 ) the images might seem to have multiple valid labels ( e . g . a tiger in a lake can be seen as “ tiger ” and “ lake ” as separate entities ), and this may cause ambiguity , and ( 3 ) the choices themselves may result in odd - one - out attacks if the correct choice is semantically different from all others . we propose an algorithm to generate the word choice set w containing unambiguous choices for the ease of users , while ensuring that word - based attacks are ineffective . for this we use a wordnet - based [ 5 ] semantic word similarity measure [ 4 ], denoted by d ( w 1 , w 2 ) where w 1 and w 2 are english words . given the correct annotation wk ( e . g . “ tiger ”) of image i k , and optionally , other words w 0 ( e . g . {“ lake ”}) with the requirement of n w choices , the algorithm for determining w is as follows : 1 . set w ←{ w k }+ w 0 , t ← 1 . 2 . choose a word w 1 ∉ w randomly from the database . 3 . flag = 0 . 4 . for each word w 1 ε w 5 . if flag = 1 then go to step 2 . 6 . w ← w +{ w 1 }; t ← t + 1 7 . if t & lt ; n w then go to step 2 . 8 . w ← w = w 0 the value of θ depends on what range of values the word similarity measure yields and can be determined empirically or based on user surveys ( i . e . what values of θ causes ambiguity ). geometrically speaking , this method yields word choices like as if all the words lie beyond the boundaries of a ( n w )- dimensional simplex or hyper - tetrahedron . distorted images produced using the 4 chosen methods in d are shown in fig5 . clearly , perceptual quality of the images have not deteriorated beyond recognition . user - friendliness of image - based captchas has been studied before [ 3 ]. hence we conducted a user survey only on the ease of use of our click - and - annotate process . we chose 8 distorted images each of 8 different concepts from the corel database , and arbitrarily chose 5 users and asked them to annotate the images ( 40 responses per concept ). on an average , 95 percent were correct responses . another survey was conducted on the ease of clicking near geometric centers in our composite images , using an 800 × 600 composite image consisting of 8 images ( r = 15 ), yielding 90 percent accuracy in user clicks . an appropriate choice of threshold t in choosing distortion set d ensures that automated annotation is not noticeably better than a random guess among the nw possible word choices . with nw = 15 , the random attack success rate for two rounds of click - and - annotate is thus ( 8 ⁢ π ⁢ ⁢ r 2 800 × 600 × 1 n w ) 2 , or 0 . 000062 percent . this is significantly lower than the attack rates of up to 99 percent on current text - based captchas . without the click phase , attack rate would still be pretty high at 1 / n w 2 or 0 . 44 percent , which justifies the need for the click phase . because cracking our proposed system will require solving two distinct hard at problems , with our design being aimed at ensuring attack - resistance to state - of - the - at image matching , we do not expect this captcha to be broken to any sizable extent in the near future , unless there is considerable progress in image understanding technology . our system generates distortions in less than 1 sec . on a 450 mhz sun ultra 60 server . word choice set takes about 20 sec . to generate using a perl interface to wordnet ( the algorithm makes iterative calls to the word similarity interface , which is slow ), but that can be sped up easily using pre - processing . in conclusion , we have invented a new captchas generation system using a considerable amount of pseudo - randomness . a novel word - choice generation algorithm is proposed that tackles issues related to user - friendliness and security . a formal method for choosing composite distortion for inclusion in the allowed distortions set is proposed , and four such distortions are obtained through experimentation . under certain assumptions about the best possible feasible attack strategy , our system is much more secure compared to text - based captchas . user - friendliness has been carefully considered in our design , and preliminary results suggest that a simple interface and just four mouse - clicks make it favorable . greater attack - resistance is possible by considering other possible attack strategies such as interest points , scale / affine invariants , and other object - recognition techniques . l . von ahn et al ., “ telling humans and computers apart ( automatically ) or how lazy cryptographers do ai ,” comm . of the acm , 47 ( 2 ): 57 - 60 , 2004 . m . chew et al ., “ image recognition captchas ,” proc . 7 th info . security conf ., 2004 . j . j . jiang et al ., “ semantic similarity based on corpus statistics and lexical taxonomy ,” proc . intl . conf . research in computational linguistics , 1997 . g . miller , “ wordnet : a lexical database for english ,” comm . of the acm , 38 ( 11 ): 39 - 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