d-anon: Automated Trespass Management

Rylan R.
rylan@turing.lol
www.d-anon.org

Abstract

d-anon is an automated trespass management platform for businesses. Using facial recognition, d-anon identifies every patron on entry, syncs trespass records across all business locations in real time, and silently alerts your security team the moment an unauthorized individual attempts to re-enter. Upon incident, d-anon makes it simple to provide evidence and report to law enforcement.

1. Introduction

Businesses suffer when items are locked up at retail. Spending drops as community centers become overrun with civil disorder. The only solution is strict, swift and just enforcement from the private sector. 

The days of the neighborhood store knowing every regular is long gone in most parts of America. Expecting high turnover staff to remember troublesome patrons is a tall order. That being said, crime and theft are still a rampant problem that needs to be solved. Systems like d-anon already exist but they are in the hands of government bodies or are industry specific , such as the notorious casino blackbook . There needs to be an open platform that any business can connect to.

2. SCOR (Shared Criminal Offense Registry) and Indexes

In addition to the internal list of trespassed patrons. Any partner organization can submit to SCOR at any time. Submissions require video evidence, which is AI-verified for accuracy before syncing to all subscribed organizations. Business owners can subscribe to shared indexes from the d-anon database, covering categories like "Theft," "Misconduct," and "Violent Offenses".  These are sourced from SCOR entries, and public court records. Activated indexes automatically flag matching individuals upon entry and trigger configurable response protocols (e.g., silent staff alerts, automatic law enforcement contact).

Detections fall into three categories:

• Green: No match, cleared to enter.
• Yellow: History of incidents at other locations.
• Red: Currently banned / trespassing.

3. Detection

Cameras are installed at every entry and exit point of the establishment, This will give a clear picture of all individuals currently on the premises. When a person of interest is detected, the response protocols will activate.

By default, individuals flagged as banned are addressed via an automated audio message through the camera system, informing them of their status and warning that remaining on the premises will be considered trespassing. From there, the business chooses how to proceed whether that means a formal trespass notice issued by security staff, or notifying local law enforcement of the individual's presence. [1]

4. Post-Incident

After an incident has occurred on premises, select the person of interest from a timestamped list of entrants and enter all known details about the incident. Then describe and label the incident, e.g. disorderly conduct or theft. After this, a copy will be sent to local law enforcement and be added to the SCOR database.

5. Privacy

The private data of entrants to an establishment is not compromised. Entrants are not added to the system and there are no logs kept of them, their facial embed will only be used to make a match against existing persons in the SCOR system. The exception being, information about an entrant will be saved upon their involvement in an incident. In applicable states, the correct precautions will be taken such as optional opt out, or affirmative consent.

6. Conclusion

Any establishment that opens itself up to the public would improve operations with our proposal. The proposed system allows all  public crime to be tracked and a historical view of a person's actions within the public to be drawn. If there is an understanding within the general public that offenses will be documented and be taken seriously, it will in turn lead to better and more orderly conduct being displayed.

[1] In the FTC’s Rite Aid enforcement action , the FTC alleged that Rite Aid’s use of facial recognition for retail security was unfair because the company failed to implement reasonable safeguards against false-positive matches and resulting consumer harm. In the FTC complaint , the FTC alleged that match alerts led to actions such as surveillance, removal from stores, public accusations, searches, blocked purchases, and police calls. The FTC order applies to Rite Aid specifically and does not categorically bar other companies from using facial-recognition security systems, but it signals that such systems should include safeguards such as accuracy testing, false-positive monitoring, staff training, complaint handling, retention/deletion controls, clear notice, and human review before adverse action.