Image Understanding & Web Security

1. Image Understanding & Web Security Henry Baird Joint work with: Richard Fateman, Allison Coates, Kris Popat, Monica Chew, Tom Breuel, & Mark Luk

2. A fast-emerging research topic Human Interactive Proofs (HIPs; definition later): • first instance in 1999 • research took hold in CS security theory field first • intersects image understanding, cog sci, etc etc • fast attracting researchers, engineers, & users This talk: • A brief history of HIPs • Existing systems -- w/ my critiques • Professional activities, so far -- incl. the 1st Int’l Workshop • In detail: PARC’s PessimalPrint & BaffleText H. Baird & K. Popat, “Web Security & Document Image Analysis,” in J. Hu & A. Antonacopoulos (Eds.), Web Document Analysis, World Scientific, 2003 (in press).

3. Straws in the wind… • 90’s: spammers trolling for email addresses • in defense, people disguise them, e.g. “baird AT parc DOT com” • 1997: abuse of ‘Add-URL’ feature at AltaVista • some write programs to add their URL many times • skewed the search rankings • Andrei Broder et al (then at DEC SRC) • a user action which is legitimate when performed once becomes abusive when repeated many times • no effective legal recourse • how to block or slow down these programs …

4. The first known instance… Altavista’s AddURL filter • 1999: “ransom note filter” • randomly pick letters, fonts, rotations – render as an image • every user is required to read and type it in correctly • reduced “spam add_URL” by “over 95%” • Weaknesses: isolated chars, filterable noise, affine deformations An image of text, not ASCII M. D. Lillibridge, M. Abadi, K. Bharat, & A. Z. Broder, “Method for Selectively Restricting Access to Computer Systems,” U.S. Patent No. 6,195,698, Filed April 13, 1998, Issued February 27, 2001.

5. Yahoo!’s “Chat Room Problem” September 2000 Udi Manber asked Prof. Manuel Blum’s group at CMU: • programs impersonate people in chat rooms, then hand out ads – ugh! • how can all machines be denied access to a Web site without inconveniencing any human users? I.e., how to distinguish between machines and people on-line … a kind of ‘Turing test’ !

6. Alan Turing (1912-1954) 1936 a universal model of computation 1940s helped break Enigma (U-boat) cipher 1949 first serious uses of a working computer including plans to read printed text (he expected it would be easy) 1950 proposed a test for machine intelligence

7. Turing’s Test for AI How to judge that a machine can ‘think’: • play an ‘imitation game’ conducted via teletypes • a human judge & two invisible interlocutors: • a human • a machine `pretending’ to be human • after asking any questions (challenges) he/she wishes, the judge decides which is human • failure to decide correctly would be convincing evidence of machine intelligence (Turing asserted) Modern GUIs invite richer challenges than teletypes…. A. Turing, “Computing Machinery & Intelligence,” Mind, Vol. 59(236), 1950.

8. “CAPTCHAs”:Completely Automated Public Turing Tests to Tell Computers & Humans Apart • challenges can be generated & graded automatically (i.e. the judge is a machine) • accepts virtually all humans, quickly & easily • rejects virtually all machines • resists automatic attack for many years (even assuming that its algorithms are known?) NOTE: the machine administers, but cannot pass the test! (M. Blum, L. A. von Ahn, J. Langford, et al, CMU-SCS) L. von Ahn, M. Blum, N.J. Hopper, J. Langford, “CAPTCHA: Using Hard AI Problems For Security,” Proc., EuroCrypt 2003, Warsaw, Poland, May 4-8, 2003 [to appear].

9. CMU’s ‘Gimpy’ CAPTCHA • Randomly pick: English words, deformations, occlusions, backgrounds, etc • Challenge user to type in any three of the words • Designed by CMU team: tried out by Yahoo! • Problem: users hated it --- Yahoo! withdrew it L. Von Ahn, M. Blum, N. J. Hopper, J. Langford, The CAPTCHA Web Page, http://www.captcha.net.

10. Yahoo!’s present CAPTCHA: “EZ-Gimpy” • Randomly pick: one English word, deformations, degradations, occlusions, colored backgrounds, etc • Better tolerated by users • Now used on a large scale to protect various services • Weaknesses: a single typeface, English lexicon

11. PayPal’s CAPTCHA • Nothing published • Seems to use a single typeface • Picks, at random: letters, overlain pattern • Weaknesses: single typeface, simple grid, no image degradations, spaced apart

12. Cropping up everywhere… • In use today, to defend against: • skewing search-engine rankings (Altavista, 1999) • infesting chat rooms, etc (Yahoo!, 2000) • gaming financial accounts (PayPal, 2001) • robot spamming (MailBlocks, SpamArrest 2002) • In the last few months:Overture, Chinese website, HotMail, CD-rebate, TicketMaster, MailFrontier, Qurb, Madonnarama, … …have you seen others? • On the horizon: • ballot stuffing, password guessing, denial-of-service attacks • `blunt force’ attacks (e.g. UT Austin break-in, Mar ’03) • …many others Similar problems w/ scrapers; also, likely on Intranets. D. P. Baron, “eBay and Database Protection,” Case No. P-33, Case Writing Office, Stanford Graduate School of Business, Stanford Univ., 2001.

13. The Known Limits ofImage Understanding Technology There remains a large gap in ability between human and machine vision systems, even when reading printed text Performance of OCR machines has been systematically studied: 7 year olds can consistently do better! This ability gap has been mapped quantitatively S. Rice, G. Nagy, T. Nartker, OCR: An Illustrated Guide to the Frontier, Kluwer Academic Publishers: 1999.

14. Image Degradation Modeling thrs x blur Effects of printing & imaging: blur thrs sens • We can generate challenging images pseudorandomly H. Baird, “Document Image Defect Models,” in H. Baird, H. Bunke, & K. Yamamoto (Eds.), Structured Document Image Analysis, Springer-Verlag: New York, 1992.

15. Machine Accuracy is a SmoothMonotonic Function of Parameters T. K. Ho & H. S. Baird, “Large Scale Simulation Studies in Image Pattern Recognition,” IEEE Trans. on PAMI, Vol. 19, No. 10, p. 1067-1079, October 1997.

16. Can You Read These Degraded Images? Of course you can …. but OCR machines cannot!

17. Experiments by PARC & UCB-CS • Pick words at random: • 70 words commonly used on the Web • w/out ascenders or descenders (cf. Spitz) • Vary physics-based image degradation parameters: blur, threshold, x-scale -- within certain ranges • Pick fonts at random from a large set: Times Roman (TR), Times Italic (TI), Palatino Roman (PR), Palatino Italic (PI), Courier Roman (CR), Courier Oblique (CO), etc • Test legibility on: • ten human volunteers (UC Berkeley CS Dept grad students) • three OCR machines: Expervision TR (E), ABBYY FineReader (A), IRIS Reader (I)

18. Results: OCR Accuracy, by machine Each machine has its peculiar blind spots

19. OCR Accuracy: varying blur & threshold The machines share some blind spots

20. Three OCR machines fail when: OCR outputs blur = 0.0 & threshold 0.02 - 0.08 threshold = 0.02 & any value of blur ~~~.I~~~ ~~i1~~ N/A N/A N/A ~~I~~ PessimalPrint: exploiting image degradations … but people find all these easy to read A. Coates, H. Baird, R. Fateman, “Pessimal Print: A Reverse Turing Test,” Proc. 6th IAPR Int’l Conf. On Doc. Anal. & Recogn. (ICDAR’01), Seattle, WA, Sep 10-13, 2001.

21. High Time for a Workshop! Manuel Blum proposes it, rounds up some key speakers Henry Baird offers PARC as venue; Kris Popat helps run it Goals: Invite all known principals: theory, systems, engineers, users Describe the state of the art Plan next steps for the field Organization: • ~30 attendees • abstracts only, 1-5 pages, no refereeing, no archival publication • 100% participation: everyone gives a (short) talk • “mixing it up”: panel & working group discussions • 2-1/2 days, lots of breaks for informal socializing • plenary talk by John McCarthy ‘Father of AI’

22. 1st NSF Int’l Workshop onHuman Interactive ProofsPARC, Palo Alto, CA, January 9-11, 2002

23. HIP’2002 Participants Altavista Andrei Broder Yahoo! Udi Manber Bell Labs Dan Lopresti IBM T.J. Watson Charles Bennett InterTrust Star Labs Stuart Haber City Univ. of Hong Hong Nancy Chan Weizmann Institute Moni Naor RSA Security Laboratories Ari Juels Document Recognition Techs, Inc Larry Spitz CMU - SCS, Aladdin Center Manuel Blum, Lenore Blum, Luis von Ahn, John Langford, Guy Blelloch, Nick Hopper, Ke Yang, Brighten Godfrey, Bartosz Przydatek, Rachel Rue PARC - SPIA/Security/Theory Henry Baird, Kris Popat, Tom Breuel, Prateek Sarkar, Tom Berson, Dirk Balfanz, David Goldberg UCB - CS & SIMS Richard Fateman, Allison Coates, Jitendra Malik, Doug Tygar, Alma Whitten, Rachna Dhamija, Monica Chew, Adrian Perrig, Dawn Song RPI George Nagy Stanford John McCarthy NSF Robert Sloan

24. Variations & Generalizations • CAPTCHA Completely Automatic Public Turing test to tell Computers and Humans Apart • HUMANOID Text-based dialogue which an individual can use to authenticate that he/she is himself/herself (‘naked in a glass bubble’) • PHONOID Individual authentication using spoken language Human Interactive Proof (HIP) An automatically administered challenge/response protocol allowing a person to authenticate him/herself as belonging to a certain group over a network without the burden of passwords, biometrics, mechanical aids, or special training.

25. Highlights of HIP’2002 • Theory • some text-based CAPTCHAs are provably breakable • Ability Gaps • vision: gestalt, segmentation, noise immunity, style consistency • speech: noise of many kinds, clutter (cocktail party effect) • intelligence: puzzles, analogical reasoning, weak logic • gestures, reflexes, common knowledge, … • Applications • subtle system-level vulnerabilties • aggressive arms race with shadowy enemies http://www.parc.com/istl/groups/did/HIP2002

26. Funding & Partnerships • NSF • Robert Sloan, Dir, Theory of Computing Pgm • strongly supportive of this newborn field • encouraged grant proposals • Yahoo! • willing to run field trials • user acceptance laboratory • able to detect intrusion

27. Disciplines Participating now: • Cryptography • Security • Pattern Recognition • Computer Vision • Artificial Intelligence • eCommerce Needed: • Cognitive Science • Psychophysics (esp. of Reading) • Biometrics • Business, Law, … • ….?

28. Weaknesses of Existing Reading-Based CAPTCHAs • English lexicon is too predictable: • dictionaries are too small • only 1.2 bits of entropy per character (cf. Shannon) • Physics-based image degradations vulnerable to well-studied image restoration attacks, e.g.  • Complex images irritate people • even when they can read them • need user-tolerance experiments

29. Strengths of Human Reading Literature on the psychophysics of reading is relevant: • familiarity helps, e.g. English words • optimal word-image size (subtended angle) is known (0.3-2 degrees) • optimal contrast conditions known • other factors measured for the best performance: to achieve and sustain “critical reading speed” BUT gives no answer to: where’s the optimal comfort zone? G. E. Legge, D. G. Pelli, G. S. Rubin, & M. M. Schleske, “Psychophysics of Reading: I. normal vision,” Vision Research25(2), 1985. A. J. Grainger & J. Segui, “Neighborhood Frequency Effects in Visual Word Recognition,’ Perception & Psychophysics 47, 1990..

30. Designing a Stronger CAPTCHA:BaffleText principles • Nonsense words. • generate ‘pronounceable’ – not ‘spellable’ – words using a variable-length character n-gram Markov model • they look familiar, but aren’t in any lexicon, e.g. ablithan wouquire quasis • Gestalt perception. • force inference of a whole word-image from fragmentary or occluded characters, e.g. • using a single familiar typeface also helps M. Chew & H. S. Baird, “BaffleText: A Human Interactive Proof,” Proc., SPIE/IS&T Conf. on Document Recognition & Retrieval X, Santa Clara, CA, January 23-24, 2003.

31. Mask Degradations Parameters of pseudorandom mask generator: • shape type: square, circle, ellipse, mixed • density: black-area / whole-area • range of radii of shapes

32. BaffleText Experiments at PARC • Goal: map the margins of accurate & comfortable human reading on this family of images • Metrics: • objective difficulty: accuracy • subjective difficulty: rating • response time • exit survey: how tolerable overall • Participation: • 41 individual sessions • >1200 challenge/response trials • 18 exit surveys

33. BaffleText challenge webpage

34. BaffleText user ratings

35. User Acceptance % Subjects willing to solve a BaffleText… 17% every time they send email 39%…if it cut spam by 10x 89% every time they register for an e-commerce site 94%…if it led to more trustworthy recommendations 100% every time they register for an email account Out of 18 responses to the exit survey.

36. Subjective difficulty tracks objective difficulty

37. How to engineer BaffleText • When we generate a challenge, • need to estimate its difficulty • throw away if too easy or too hard • Apply an idea from the psychophysics of reading: • image “complexity” metric: how hard to read • simple to compute: perimeter** / black-area

38. Image complexity predicts objective difficulty

39. Image complexity predicts subjective difficulty

40. 50 100 Engineering guidelines • For high performance, image complexity should fall in the range 50-100; e.g. • Within this regime, BaffleText performs well: • 100% human subjects willing to try to read it • 89% accuracy by humans • 0% accuracy by commercial OCR • 3.3 difficulty rating, out of 10 (on average) • 8.7 seconds / trial on average

41. G. Mori & J. Malik, “Recognizing Objects in Adversarial Clutter,” submitted to CVPR’03, Madison, WI, June 16-22, 2003. The latest serious (known or published) attack… Greg Mori & Jitendra Malik (UCB-CS) • Generalized Shape Context CV method • requires known lexicon – else, fails completely • expects known font (or fonts) – else, does worse Results of Mori-Malik attacks (Dec 2002) given perfect foreknowledge of both lexicon and font:

42. BaffleText: the strongest known CAPTCHA? • Resists many known algorithmic attacks: • physics-based image restoration • recognizing into a lexicon • known-typeface targeting • segmenting then recognizing • Exploits hard-to-automate human cognition powers: • Gestalt perception • “semi-linguistic” familiarity • within-typeface “style consistency”

43. Recent Microsoft CAPTCHA • Random strings, local space-warping; plus meaningless curving strokes, both black (overlaid) and white (erasing) • Fielded Dec 2002 on Passport (HotMail, etc) • Immediate reduction in new Hotmail accounts, with virtually no user complaints P. Y. Simard, R. Szeliski, J. Benaloh, J. Couvreur, I. Calinov, “Using Character Recognition and Segmentation to Tell Computer from Humans,” Proc., Int’l Conf. on Document Analysis & Recognition, Edinburgh, Scotland, August, 2003 [to appear].

44. PARC’s Leadership in R&D on Reading-based CAPTCHAs • First refereed article on CAPTCHAs: A. L. Coates, H. S. Baird, R. Fateman, “Pessimal Print: a Reverse Turing Test,” Proc., 6th IAPR Int’l Conf. On Document Analysis & Recognition, Seattle, WA, Sept. 10-13, 2001. • First professional HIP event, organized by PARC:1st NSF Int’l Workshop on HIPs, Jan. 9-11, 2002, PARC, Palo Alto, CA. • First to ‘play both offense & defense’: • builds high-performance OCR systems; attacks CAPTCHAs • builds strong CAPTCHAs • First to validate using human-factors research: • human-subject trials measuring both accuracy & tolerance • PARC’s interdisciplinary tradition: social + computer sciences

45. The Arms Race • When will serious technical attacks be launched? • ‘spam kings’ make $$ millions • two spam-blocking e-commerce firms now use CAPTCHAs • How long can a CAPTCHA withstand attack? • especially if its algorithms are published or guessed • Strategy: keep a pipeline of defenses in reserve: • continuing partnership between R&D & users

46. Lots of Open Research Questions • What are the most intractable obstacles to machine vision? segmentation, occlusion, degradations, …? • Under what conditions is human reading most robust? linguistic & semantic context, Gestalt, style consistency…? • Where are ‘ability gaps’ located? quantitatively, not just qualitatively • How to generate challenges strictlywithin ability gaps? fully automatically an indefinitely long sequence of distinct challenges

47. HIP Research Community • PARC CAPTCHA website www.parc.com/istl/projects/captcha • HIP’2002 Workshop www.parc.com/istl/groups/did/HIP2002 • HIP Website at Aladdin Center, CMU-SCS www.captcha.net • Volunteers for a PARC CAPTCHA usability test? • A 2nd HIP Workshop soon?

48. Alan Turing might have enjoyed the irony … A technical problem – machine reading – which he thought would be easy, has resisted attack for 50 years, and now allows the first widespread practical use of variants of his test for artificial intelligence.

49. Contact Henry S. Baird baird@parc.com www.parc.com/baird