Hackathon Highlights:

Challenge Impact: Supporting cybersecurity and promoting AI-powered face spoofing detection in Zimbabwe and Sub-Saharan Africa. This challenge addresses the critical need for secure facial-recognition systems that can distinguish between real faces and spoofed attempts (photos, videos, masks, or deepfakes).

Event Details: Please note: participants must first register for the “Intro to Zindi” session before gaining access to the hackathon. Only registered users will receive the access code for the hackathon event.

Registration Link: https://luma.com/5vb4i27o

Timeline:

• Registration Opens: 24 November 2025 at 10:00 AM CAT
• Intro to Zindi Session: 29 November 2025 – 10:00 AM CAT
• Mini Hackathon: 29 November 2025 (11:30 AM CAT) – 1 December 2025 (11:59 PM CAT)
• Results Announcement: 4 December 2025

Eligibility: Open exclusively to all Zimbabweans.

The Challenge

Facial recognition technology has become increasingly prevalent across various sectors in Zimbabwe and Sub-Saharan Africa, from mobile-device security and banking authentication to access-control systems and border security. However, as these systems become more widespread, they face a growing threat from face-spoofing attacks—attempts to deceive facial-recognition systems using printed photos, video replays, 3D masks, silicone faces, or AI-generated deepfakes.

Face-spoofing poses significant security risks across multiple domains. In mobile banking and financial services, spoofed authentication could lead to unauthorized access to accounts and financial fraud. In access-control systems, spoofing attacks could compromise building security. In border control and identity verification, spoofed faces could enable identity theft or unauthorized entry. The sophistication of spoofing techniques continues to evolve, with deepfake technology making it increasingly difficult to distinguish between real and fake faces using traditional methods.

Current face-spoofing detection systems often rely on single-modal approaches (color images only) or require expensive specialized hardware, making them less accessible in resource-constrained environments. Many existing solutions lack robustness to real-world conditions such as varying lighting, angles, and image quality. This creates a critical gap in cybersecurity infrastructure, particularly in regions where secure authentication is essential for financial inclusion and digital transformation.

Objective of the Challenge This challenge aims to harness the power of deep-learning to develop robust face-spoofing detection solutions using multi-modal data (color and depth images). Participants are required to build and train models that can accurately classify face images as either “real” (authentic) or “fake” (spoofed).

The models must:

• Accurately distinguish between real faces and various spoofing attacks (photos, videos, masks, deepfakes)
• Be robust to real-world conditions (variable lighting, face angles, image quality)
• Provide well-calibrated probability predictions suitable for security applications
• Leverage both color and depth information effectively for enhanced detection accuracy

Relevance of the Solution This initiative addresses a pressing cybersecurity challenge in Zimbabwe and Sub-Saharan Africa—the need for accessible, robust face-spoofing detection systems. By automating spoofing detection with AI, this solution can:

• Enhance mobile-banking security, protecting users from financial fraud and enabling secure digital financial services
• Strengthen access-control systems, improving security for buildings, facilities, and sensitive areas
• Support identity-verification systems, reducing identity theft and unauthorized access
• Enable secure authentication in resource-constrained environments without requiring expensive specialized hardware
• Promote trust in facial-recognition technology, encouraging adoption of secure digital services

Furthermore, this challenge promotes the development of AI-powered cybersecurity solutions tailored to African contexts. It provides an opportunity to demonstrate how deep-learning can solve real-world security problems while building local capacity in cybersecurity and AI.

By participating, you are not only showcasing technical skills but also contributing to a more secure digital future for Africa.

This challenge has been organised by Shannon T Sikadi - Zindi Ambassador, Zimbabwe (Email: shannonsikadi[@ gmail.com], Phone: +263 785 273 98), and is also in partnership with Deep Learning IndabaX Zimbabwe.

Evaluation :

The challenge will be evaluated uisng Log Loss, ensuring models provide reliable probability estimates that are crucial for security applications where confidence levels matter as much as binary predictions.

Your submission file should look like:

image_id real fake   
image_01 0.3357 0.6643   
image_02 0.6233 0.3767   
image_03 0.4651 0.5349

Prize Money:

USD 50 will be awarded to the top performer.

This challenge is open to anyone from Zimbabwe.

Teams and collaboration

You may participate in competitions as an individual or in a team of up to two people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the total number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.

Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be immediately disqualified from the platform.

Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).

The Zindi data scientist who sets up a team is the default Team Leader but they can transfer leadership to another data scientist on the team. The Team Leader can invite other data scientists to their team. Invited data scientists can accept or reject invitations. Until a second data scientist accepts an invitation to join a team, the data scientist who initiated a team remains an individual on the leaderboard. No additional members may be added to teams within the last hour of a hackathon.

The team leader can initiate a merge with another team. Only the team leader of the second team can accept the invite. The default team leader is the leader from the team who initiated the invite. Teams can only merge if the total number of members is less than or equal to the maximum team size of the competition.

A team can be disbanded if it has not yet made a submission. Once a submission is made individual members cannot leave the team.

All members in the team receive points associated with their ranking in the competition and there is no split or division of the points between team members.

Datasets and packages

The solution must use publicly-available, open-source packages only.

If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.

You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.

You may use pretrained models as long as they are openly available to everyone.

The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.

You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.

Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.

Submissions and winning

You may make a maximum of 40 submissions per day.

You may make a maximum of 100 submissions for this competition.

Before the end of the competition you need to choose 2 submissions to be judged on for the private leaderboard. If you do not make a selection your 2 best public leaderboard submissions will be used to score on the private leaderboard.

During the competition, your best public score will be displayed regardless of the submissions you have selected. When the competition closes your best private score out of the 2 selected submissions will be displayed.

Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 20% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 80% of the test dataset, will be made public and will constitute the final ranking for the competition.

Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.

If you are in the top 5 at the time the leaderboard closes, we will email you to request your code. On receipt of email, you will have 2 hours to respond and submit your code following the Reproducibility of the submitted code guidelines detailed below. Failure to respond will result in disqualification.

If your solution places 1st, 2nd, or 3rd on the final leaderboard, you will be required to submit your winning solution code to us for verification, and you thereby agree to assign all worldwide rights of copyright in and to such winning solution to Zindi.

If two solutions earn identical scores on the leaderboard, the tiebreaker will be the date and time in which the submission was made (the earlier solution will win).

The winners will be paid via bank transfer, PayPal if payment is less than or equivalent to $100, or other international money transfer platform. International transfer fees will be deducted from the total prize amount, unless the prize money is under $500, in which case the international transfer fees will be covered by Zindi. In all cases, the winners are responsible for any other fees applied by their own bank or other institution for receiving the prize money. All taxes imposed on prizes are the sole responsibility of the winners. The top winners or team leaders will be required to present Zindi with proof of identification, proof of residence and a letter from your bank confirming your banking details. Winners will be paid in USD or the currency of the competition. If your account cannot receive US Dollars or the currency of the competition then your bank will need to provide proof of this and Zindi will try to accommodate this.

Please note that due to the ongoing Russia-Ukraine conflict, we are not currently able to make prize payments to winners located in Russia. We apologise for any inconvenience that may cause, and will handle any issues that arise on a case-by-case basis.

Payment will be made after code review and sealing the leaderboard.

You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.

Zindi is committed to providing solutions of value to our clients and partners. To this end, we reserve the right to disqualify your submission on the grounds of usability or value. This includes but is not limited to the use of data leaks or any other practices that we deem to compromise the inherent value of your solution.

Zindi also reserves the right to disqualify you and/or your submissions from any competition if we believe that you violated the rules or violated the spirit of the competition or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.

Please refer to the FAQs and Terms of Use for additional rules that may apply to this competition. We reserve the right to update these rules at any time.

Reproducibility of submitted code

• If your submitted code does not reproduce your score on the leaderboard, we reserve the right to adjust your rank to the score generated by the code you submitted.
• If your code does not run you will be dropped from the top 5. Please make sure your code runs before submitting your solution.
• Always set the seed. Rerunning your model should always place you at the same position on the leaderboard. When running your solution, if randomness shifts you down the leaderboard we reserve the right to adjust your rank to the closest score that your submission reproduces.
• Custom packages in your submission notebook will not be accepted.
• You may only use tools available to everyone i.e. no paid services or free trials that require a credit card.

Documentation

A README markdown file is required

It should cover:

• How to set up folders and where each file is saved
• Order in which to run code
• Explanations of features used
• Environment for the code to be run (conda environment.yml file or an environment.txt file)
• Hardware needed (e.g. Google Colab or the specifications of your local machine)
• Expected run time for each notebook. This will be useful to the review team for time and resource allocation.

Your code needs to run properly, code reviewers do not have time to debug code. If code does not run easily you will be bumped down the leaderboard.

Consequences of breaking any rules of the competition or submission guidelines:

• First offence: No prizes for 6 months and 2000 points will be removed from your profile (probation period). If you are caught cheating, all individuals involved in cheating will be disqualified from the challenge(s) you were caught in and you will be disqualified from winning any competitions for the next six months and 2000 points will be removed from your profile. If you have less than 2000 points to your profile your points will be set to 0.
• Second offence: Banned from the platform. If you are caught for a second time your Zindi account will be disabled and you will be disqualified from winning any competitions or Zindi points using any other account.
• Teams with individuals who are caught cheating will not be eligible to win prizes or points in the competition in which the cheating occurred, regardless of the individuals’ knowledge of or participation in the offence.
• Teams with individuals who have previously committed an offence will not be eligible for any prizes for any competitions during the 6-month probation period.

Monitoring of submissions

• We will review the top 5 solutions of every competition when the competition ends.
• We reserve the right to request code from any user at any time during a challenge. You will have 24 hours to submit your code following the rules for code review (see above). Zindi reserves the right not to explain our reasons for requesting code. If you do not submit your code within 24 hours you will be disqualified from winning any competitions or Zindi points for the next six months. If you fall under suspicion again and your code is requested and you fail to submit your code within 24 hours, your Zindi account will be disabled and you will be disqualified from winning any competitions or Zindi points with any other account.