Table 2 Typical image-based CAPTCHAs
From: Human-artificial intelligence approaches for secure analysis in CAPTCHA codes
CAPTCHA | Illustration | Challenge | Feature | Type |
|---|---|---|---|---|
CAPTCHaStar [32] |
| Move the cursor until a recognizable shape is formed | White pixels, noise, background, shape changing in term of moving cursor’s location | Interactive-based |
Noise CAPTCHA [33] |
| Move a small noisy image on top of a large noisy image until a hidden message or object appears | Noise, background, shape changing in term of moving cursor’s location | |
Cursor CAPTCHA [34] |
| Overlap the cursor on the target object in a randomly generated image | Background, noise, random location of target | |
Asirra [35] |
| Choose a cat from a collection of 12 images of cats and dogs | Grids, categorization of cats and dogs, location API integration (hence, poisoned database attacks) | Selection-based |
HumanAuth CAPTCHA [36] |
| Choose images that have natural content | Limited image database, grids, masking images with logo | |
SEMAGE CAPTCHA [37] |
| Choose images that are semantically related from a set of images | Grids, limited image database, semantic linking | |
No captcha reCAPTCHA [38] |
| Choose all images that contain a specific object | Grids, object recognition, user activity tracking | |
Avatar CAPTCHA [39] |
| Choose an avatar face from a set of 12 images that include both human and avatar faces | Limited image database, grids, grayscale | |
FaceDCAPTCHA [40] |
| Choose two images of the same person's face | Limited image database, noise, background, random image positions, rotation | |
FR-CAPTCHA [41] |
| Choose distorted real human faces from among nonhuman face images | Limited image database, noise, background, random image positions, rotation, distortion | |
Implicit CAPTCHA [42] |
| Click on a specific area of an image | Limited image database, human craft, single target | Click-based |
SACaptcha [43] |
| Click on some of the image's regions that contain a specific shape mentioned in the challenge description | Limited image database, human craft, multi targets | |
Drawing CAPTCHA [43] |
| Connect specific dots to one another | Noise, texture background, drawing | Draw-based |
VAPTCHA [44] |
| Draw a similar trajectory to the reference trajectory | Noise, background, drawing patterns | |
MotionCAPTCHA [45] |
| Draw the shape shown in the box | Noise, background, drawing patterns | |
WHAT's Up CAPTCHA [46] |
| Slide the slider to the right to reorient at least three randomly rotated images | Three circle cells, limited image database, rotation | Slide-based |
Minteye's Slide-to-Fit CAPTCHA [47] |
| Slide the slider until an undistorted image appears | Distortion, rotation, background, noise | |
Tencent CAPTCHA |
| Move the slider such that two puzzle pieces match | Background, two puzzle pieces | |
Garb CAPTCHA [48] |
| To reconstruct the original image, drag and drop the puzzle pieces into their proper positions | Multi layers, background, noise, multi puzzle pieces, random positions | Drag and drop based |
Capy CAPTCHA [49] |
| Drag a puzzle piece to finish a jigsaw puzzle | Multi layers, background, noise, one puzzle piece | |
KeyCAPTCHA [50] |
| Drag three puzzle pieces to put the image together | Multi layers, background, noise, three puzzle pieces |
