BeCAPTCHA-Mouse Benchmark

BeCAPTCHA-Mouse Benchmark for development of bot detection technologyes based on mouse dynamics.

INSTRUCTIONS FOR DOWNLOADING BeCAPTCHA-Mouse Benchmark

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   Subject: [DATABASE benchmark: BeCAPTCHA_Mouse_Benchmark]

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DESCRIPTION OF BeCAPTCHA-Mouse BENCHMARK

BeCAPTCHA-Mouse benchmark contains 9K synthetic mouse trayectories generated with two methods:

Method 1: Function-based Mouse Trajectory Synthesis
With this approach the mouse trajectories are generated according to three different trajectory shapes (linear, quadratic, and exponential) and three different velocity profiles (constant, logarithmic, and Gaussian). To generate a synthetic trajectory {x̂, ŷ} with M points, first we define the initial point [x̂₁, ŷ₁] and ending point [x̂_M, ŷ_M]. Second, we select one of three velocity profiles: i) constant velocity, where the distance between adjacent points is constant; ii) logarithmic velocity, where the distances are gradually increasing (acceleration); and iii) Gaussian velocity, in which the distances first increase and then decrease when they get close to the end of the trajectory (acceleration and deceleration). Third, we generate a sequence x̂ between x̂₁ and x̂_M spaced according to the selected velocity profile. The ŷ sequence is then generated according to the shape function. For example, for a shape defined by the quadratic function ŷ = ax̂² + bx̂ + c, we fit b and c for a fixed value of a by using the initial and ending points. We repeat the process fixing either b or c. The range of the parameters {a, b, c} explored is determined by analyzing real mouse movements fitted to quadratic functions. Linear and exponential shapes are generated similarly.
Fig. 1 (trajectories D, E, and F) shows some examples of these mouse trajectories synthesized. That figure also shows the 3 diferent velocity profiles considered: the 3 trajectories in E have constant velocity, F shows acceleration (the distance between adjacent samples increases gradually), and D has initial acceleration and final deceleration. We can generate infinite mouse trajectories with this approach by varying the parameters of each function.

Figure 1. Examples of mouse trajectories and their velocity profiles employed in this work: A is a real one extracted from a task of the database; B and C are synthetic trajectories generated with the GAN network; D, E and F are generated with the knowledge-based approach. Note that for each velocity profile (D = Gaussian, E = constant, F = logarithmic), we include the three knowledge-based trajectories (linear, quadratic, and exponential).

Method 2: GAN-based Trajectories
For this approach we employ a GAN (Generative Adversarial Network),in which two neuronal networks, commonly named Generator and Discriminator, are trained one against the other (thus the \adversarial"). The architecture of the GAN is depicted in Fig. 2.

Figure 2. The proposed architecture to train a GAN Generator of synthetic mouse trajectories.The Generator learns the human features of the mouse trajectories and generate human-like ones from Gaussian Noise.

The aim of the Generator is to fool the Discriminator by generating synthetic mouse trajectories very similar to the real ones, while the Discriminator has to predict whether the sample comes from the real set or is a fake created by the Generator. Once the Generator is trained this way, then we can use it to synthesize mouse trajectories very similar to the human ones. Fig. 1 shows two examples (trajectories B and C) of synthetic mouse trajectories generated with the GAN network and the comparison with a real one.
The human mouse trajectories employed to train the GAN network were extracted from Chao et al. [2] database, which is comprised of more than 200K mouse trajectories acquired from 58 users who completed 300 repetitions of the task. In each repetition, the users had to click 8 buttons that appeared in the screen sequentially. This task was repeated twice in each session.

BENCHMARK STRUCTURE

BeCAPTCHA-Mouse benchmark are composed by two main folders: 'DB_GAN' which contains the synthetic GAN trayectories and 'DB_fnc' that contains the function-based ones. Each main folder has other two folders: 'raw' folder which contains the raw data of the synthetic mouse trayectories in .txt files, and 'neuromotor' folder that contains the Sigma-Lognormal descomposition (more details in [3]) of the raw files in .ana format. Both kind of files have the same name to match them easily.

FILES FORMAT

• .txt files: it just contains two columns with the {x̂, ŷ} mouse coordinates.

  • COLUMN 1: represents the x̂ coordinate.

  • COLUMN 2: represents the ŷ coordinate.

• .ana files: each row contains a log-normal signal extracted from the synthetic mouse trayectory, this log-normal signal is definded by 6 parameters. One parameter in each column:

  • COLUMN 1: represents the D parameter.

  • COLUMN 2: represents the t₀ parameter.

  • COLUMN 3: represents the μ parameter.

  • COLUMN 4: represents the σ parameter.

  • COLUMNS 5 : represents the θ_s parameter.

  • COLUMNS 6 : represents the θ_e parameter.

  • COLUMNS 7, 8: are zeros.

FILES NOMENCLATURE

The nomenclature followed to name the files of the function-based method is: NNNN_y=A_vp=B_task=C.txt

• NNNN: indicates the number of the sample.

• A: indicates the shape of the trajectory:

  • 0 = linear.

  • 1 = quadratic.

  • 2 = exponential.

• B: indicates the velocity profile:

  • 0 = constant velocity.

  • 1 = logarithmic velocity.

  • 2 = Gaussian velocity.

• C: indicates the task (1-8) of the human mouse database in which the trayectory was synthetized. This is necessary because the function-based method needs the initial [x̂₁, ŷ₁] and the end [x̂_M, ŷ_M] points of the human trayectory to synthetyse.

REFERENCES

For further information on the benchmark and on different applications where it has been used, we refer the reader to (all these articles are publicly available in the publications section of the BiDA group webpage).

• [1] A. Acien, A. Morales, J. Fierrez, R. Vera-Rodriguez. BeCAPTCHA-Mouse: Synthetic Mouse Trajectories and Improved Bot Detection. arXiv:2005.00890, 2020. [pdf]

• [2] C. Shen, Z. Cai, X. Guan, R. Maxion. Performance evaluation of anomalydetection algorithms for mouse dynamics, Computers & Security, 45: 156–171, 2014.

• [3] M. Djioua, R. Plamondon. A new algorithm and system for the characterization of handwriting strokes with delta-lognormal parameters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11): 2060–2072, 2009.

Please remember to reference article [1] on any work made public, whatever the form, based directly or indirectly on any part of the BeCAPTCHA-Mouse benchmark.