DeltaForce-OBS-Locker: A Game-Cheat Repository Framed as an OBS Course

What the DeltaForce-OBS-Locker repository actually ships

The README describes a Python project that watches a game window, detects human-shaped targets with a YOLO-omni model, and moves the mouse toward them. It is split into a desktop folder and a mobile folder, plus two linked backup repositories named deltaforce-pc and deltaforce-mobile. The stated audience is Python beginners and computer vision hobbyists, and the stated purpose is technical teaching and principle verification. The README also says the plugin does not modify game memory and relies only on public image recognition and input simulation APIs. That distinction matters: memory reading and process injection are the usual signature of a game cheat, while screen capture plus synthetic mouse input sits in a greyer zone that anti-cheat vendors still act on. The repository's own warning line says it is not recommended for use in any real match, which is the most honest sentence in the document. Anyone arriving from the Topics list, which reads cv, deep-learning, obs, obs-studio, should understand that the cv and deep-learning labels are accurate and the obs label is the delivery mechanism, not the subject.

The OBS capture to SendInput pipeline

The mechanism visible in the material is a loop: OBS provides the rendered frame, a YOLO-omni model produces detections, a filtering layer removes non-human contours, and a smoothing stage converts the detection coordinates into mouse movement. The README names SendInput as the Windows input API on the desktop side and describes simulated casting and reeling for the auto-fishing module. It also mentions a visual-centre heuristic that treats the centre of a flashlight spot as the target point, and multi-frame voting to reduce noise. The V4 notes describe a contour filter that excludes container protective suits on the Nuke Plant AZ3 map after V3 misclassified them as players. That is a concrete example of a false positive class being patched by adding a negative label rather than retraining from scratch. The V5 notes extend the same pattern to the Tide Prison map, to Silver Wing operator effects, and to legendary skin features, which are down-weighted to reduce false alarms. Read as engineering, this is an incremental whack-a-mole against a shifting visual domain. Read as a product, it is a model that must be re-tuned every season, and the README says so itself: static methods fail quickly once game security policies update.

Installing it: the Star, Fork, Download ritual and the real commands

The primary repository does not document a normal install. It asks you to star the repo, fork it to your own account, then use Code, Download ZIP. The README warns that empty files after download mean you skipped a step, which suggests the repository is deliberately incomplete until forked. For the backup repositories the commands are explicit. On the PC side: git clone https://github.com/ace-trump-tech/deltaforce-pc.git, then cd deltaforce-pc, python -m venv .venv, python -m pip install --upgrade pip, python -m pip install -r requirements.txt, python gui.py. On the mobile side: git clone https://github.com/ace-trump-tech/deltaforce-mobile.git, then cd deltaforce-mobile, create the venv the same way, install requirements, and run python download_apk.py --repo-id <owner>/<repo> --filename <path/to/file.apk>. The mobile script deliberately does not hardcode an APK URL, so you must supply a Hugging Face repository and file path yourself, and the README tells you to check the publisher's checksum and signature before installing. Both sides require the YOLO-omni weights, which the README links to github.com/z637826/yolo-omni and says must be updated if downloaded before August 25. There is no requirements.txt content, no config file schema and no model path convention shown in this README, so the actual weight placement instructions live in the two sub-repository READMEs, which are not included here.

Where the design breaks: detection drift and the missing licence file

The most obvious failure mode is seasonal drift. The README's own version history shows V3 misreading protective suits as players, V4 adding a filter for that map, and V5 adding filters for a new map, a new operator and a new skin tier. Each patch is a response to a specific false positive, which means the model has no general guarantee against the next one. The second failure is dependency on an external weight repository. If github.com/z637826/yolo-omni moves or the weights change, the pipeline stops with a missing-model error, and the README gives no checksum for the weights. The third is the licence. The README ends with an MIT License line and a clause forbidding commercial cheat software, but the repository metadata supplied here lists the licence as unknown and no LICENSE file is confirmed. An MIT grant cannot be read alongside a use restriction without tension, and the mobile side distributes an APK whose own licence is not stated. The fourth is the anti-cheat context. The README claims stable detection evasion through dynamic paths and multi-frame voting, but it also admits the game's security updates invalidate static methods. You cannot plan around that; you can only re-patch.

How this differs from a normal OBS plugin

A conventional OBS Studio plugin is a compiled module that registers a source, filter or output through the OBS API, and it is installed into the OBS plugin directory. This project does not do that. It treats OBS as a screen source and drives the mouse from outside OBS, so the plugin is not a plugin in the OBS sense at all. The practical difference is where the risk sits. An OBS filter that colour-corrects a capture cannot get an account banned; a process that reads the capture and injects mouse movement can. If your actual goal is to learn OBS plugin development, this repository teaches you almost nothing about the OBS module ABI, the source registration path or the filter graph. If your goal is to learn YOLO inference on a live screen feed, it is a usable reference, and the V4 contour-filtering episode is a decent case study in handling a specific false positive class. The two goals are not served by the same code, and the repository's framing blurs them.

Maintenance cost and the licence question

Every season in the README's history produces a new major version: V3 to V4 for the S10 Nuke Plant map, V4 to V5 for S11 Echo with Tide Prison, Silver Wing isolation and ACE 4.0. That is roughly one forced update per season, plus weight updates whenever the external yolo-omni repository changes. The mobile side adds a second maintenance axis, because the download script requires you to name a Hugging Face repository and file path that the project does not control, and the README asks you to verify checksums and signatures yourself. On licensing, the README states MIT and permits modification and secondary development while forbidding commercial cheat use. MIT does not normally allow that kind of field-of-use restriction, so either the licence file differs from the README text or the README text is aspirational. The repository metadata available here does not resolve this. If you plan to reuse any code, read the actual LICENSE file in the tree rather than the README paragraph. This is not legal advice; it is a note that the two sources disagree.

Who this repository is for, and who should close the tab

The README's stated audience is Python beginners and computer vision hobbyists, and for that audience the useful parts are narrow but real: the OBS capture loop, the YOLO-omni inference call, the multi-frame voting buffer, and the V4 write-up of filtering a specific false positive class. Anyone studying game-vision pipelines can read those files without running the aim-assist path. Everyone else should stay away. Competitive players who value their account should not install this, and the README's own warning line agrees. Developers looking for an OBS plugin should look at the OBS Studio plugin template instead, because this project never registers with OBS. Anyone who needs a maintained, versioned dependency should note that the project has no releases, no homepage, and a licence that is listed as unknown in the metadata. The honest summary is that this is a cheat repository with a tutorial wrapper, and the wrapper is the only part worth reading.

Editorial conclusion

Adopt this only as a reading exercise in computer vision plumbing: OBS capture, YOLO-omni inference, SendInput simulation. Do not adopt it as a game tool, and do not treat the MIT line in the README as a clearance to redistribute the bundled APK. Before cloning, verify three things: the yolo-omni weights link at github.com/z637826/yolo-omni still resolves, the deltaforce-pc and deltaforce-mobile backup repos exist under ace-trump-tech, and the licence file actually present in the tree matches the MIT claim. If any of those three fails, the install path described in the README is already broken.

Official sources

1. ace-trump-tech/DeltaForce-OBS-Locker on GitHub
2. Issues
3. README