PhishDecloaker Datasets

Phishing Kit Dataset

• Section: 2
• Description: For empirical study. 
• Contents: 100 defanged PHP phishing kits representing the following list of brands

1. Microsoft

2. Banco de Oro

3. Microsoft OneDrive

4. Deutsche Kreditbank

5. Adobe Acrobat

6. N26

7. Absa Group

8. DHL

9. Microsoft

10. Correos

11. Kempinski Summerland Hotel & Resort Beirut

12. Vantage West Credit Union

13. NetFlix

14. Agencia Tributaria

15. Square

16. Chronopost

17. PayPal

18. American Express

19. Allegro

20. LinkedIn

21. virtru

22. Citibank

23. AOL

24. Credit Agricole

25. Mercado Pago

26. Université de Pau et des Pays de l'Adour (UPPA)

27. Fifth Third Banki

28. Columbia Bank

29. Alibaba Mail

30. Microsoft OneDrive

31. Intesa Sanpaolo

32. Santander

33. America First Credit Union

34. Barclays

35. Interac

36. USPS

37. Wells Fargo

38. Yahoo

39. XFINITY

40. Berliner Sparkasse

41. OneDrive

42. Standard Bank

43. Wells Fargo

44. aruba.it

45. Bancolombia

46. Caisse d’Epargne

47. DubaiPay

48. Chase Bank

49. M&T Bank

50. Postmaster

51. Volksbanken Raiffeisenbanken

52. Facebook

53. Huntington Bank

54. Commonwealth Bank of Australia

55. Orange

56. shopify

57. Google Drive

58. WalletConnect

59. Meritrust Credit Union

60. Credit Agricole

61. Desjardins

62. Postbank

63. Dropbox

64. DocuSign

65. dpdgroup

66. L'Assurance Maladie

67. Adobe Acrobat

68. Global Sources

69. Microsoft Excel

70. SFR

71. FedEx

72. Citibank

73. Royal Credit Union

74. GoDaddy

75. ADP

76. International Card Services

77. Israeli Post

78. UNI Financial Cooperation

79. TD Bank

80. ATB Mobile

81. HSBC

82. Bank of Montreal

83. RBC Royal Bank

84. IONOS

85. AlaskaUSA Federal Credit Union

86. French Government

87. UOL SAC

88. Banco Itaú Paraguay

89. Amazon

90. Apple

91. AT&T

92. Australian Government

93. Bank of America

94. BNP Paribas

95. eBay

96. ING Group

97. Instagram

98. MetaMask

99. SingTel

100. Société Générale

Landscape Dataset

• Section: 4.3
• Description: For training the rotation CAPTCHA solver model.
• Contents: 7,268 natural and man-made landscape images (320×180).
• Format: JPEG images.

CAPTCHA Detection Dataset

• Section: 5.2.1
• Description: For training the CAPTCHA detection model.
• Contents: 19,680 webpage screenshots (1920×1080), 10,680 with annotated CAPTCHA bounding boxes, 9,000 without.
• Format: PNG images with annotations in PASCAL VOC and COCO format.All bounding boxes are labeled as the "CAPTCHA" class (no CAPTCHA type categorization).

CAPTCHA Recognition Dataset

• Section: 5.2.2
• Description: For training the CAPTCHA recognition model
• Contents: 6,612 CAPTCHA images distributed across 38 classes.
• Format: PNG images with their corresponding class labels in CSV

CAPTCHA classes:

1. baidu_slide_rotate

2. dingxiang_audio

3. dingxiang_click_area

4. dingxiang_click_difference

5. dingxiang_click_font

6. dingxiang_click_icon

7. dingxiang_click_vr

8. dingxiang_click_word

9. dingxiang_drag

10. dingxiang_slide_puzzle

11. dingxiang_slide_puzzle2

12. dingxiang_slide_rotate

13. geetest_checkbox

14. geetest_click_icon

15. geetest_click_phrase

16. geetest_click_word

17. geetest_game_playing

18. geetest_game_playing2

19. geetest_select

20. geetest_slide_puzzle

21. hcaptcha

22. hcaptcha_checkbox

23. netease_click_icon

24. netease_click_phrase

25. netease_click_vr

26. netease_click_word

27. netease_drag

28. netease_slide

29. press_and_hold

30. recaptchav2

31. recaptchav2_checkbox

32. tencent_slide

33. text_1

34. text_2

35. text_3

36. text_4

37. text_5

38. text_6

CAPTCHA Open-set Dataset

• Section: 5.2.2
• Description: For testing the CAPTCHA detection and recognition pipeline.
• Contents: 1,100 webpage screenshots (1920×1080), all of which have annotated CAPTCHA classes spanning 11 different categories.
• Format: PNG CAPTCHA and screenshot images with their corresponding class labels in CSV

CAPTCHA classes:

1. arkose_select_2

2. capycaptcha_drag

3. dicecaptcha_qa

4. funcaptcha_select

5. funcaptcha_select_2

6. funcaptcha_select_3

7. funcaptcha_select_4

8. funcaptcha_select_5

9. funcaptcha_select_6

10. keycaptcha_drag

11. mtcaptcha_text

Ablation Dataset

• Section: 5.4
• Description: For training the CAPTCHA recognition model
• Contents: 722 webpage screenshots (1920×1080), 622 with CAPTCHAs spanning 38 classes, 100 without.
• Format: PNG images with their corresponding bounding box and class labels in CSV. Class IDs 0-37 can be directly mapped to class names in CAPTCHA recognition dataset. Class ID 38 are samples without CAPTCHAs.