This is a Go-based video parsing tool that removes watermarks from videos across 20+ Chinese social media platforms (Douyin, Kuaishou, Bilibili, Weibo, etc.). It provides three interfaces: a CLI tool (using Cobra subcommands `serve`, `parse`, `id`, `version`), an HTTP API (Gin-based with v1 RESTful endpoints and legacy backward-compatible routes), and a reusable Go library. The architecture is a monolith with a clean separation: `parser/` package contains platform-specific parsers implementing strategy pattern interfaces, `cmd/` package handles CLI and HTTP server logic with a middleware stack (recovery, CORS, rate limiting, basic auth, logging), and `utils/` provides URL extraction. The system is stateless, uses no database, and relies on HTTP scraping and API calls to extract video metadata. Notable engineering craftsmanship includes a custom anti-crawling a_bogus signature generation for Douyin's native detail API (SM3 hash, RC4 encryption), a concurrent-safe proxy injection via atomic.Value, and a well-tested middleware stack with IP-based rate limiting using sync.Map and periodic cleanup. The codebase has comprehensive unit and integration tests, CI/CD pipelines, and Docker support.
A comprehensive tool for removing watermarks from Gemini AI-generated images and videos. It provides multiple deployment options: a Chrome extension, a Tampermonkey userscript, an npm package (SDK), a CLI, and an AI agent skill. The core algorithm uses mathematically exact reverse alpha blending with pre-calibrated alpha maps to remove the Gemini logo without degrading underlying image quality. The system includes a sophisticated pipeline for detection, candidate selection, alpha trial, repair, and quality evaluation, with support for both image and video watermark removal. The repository is hybrid: while primarily an application, it contains an extensive set of data/research scripts for benchmarking, alpha profile analysis, video residual analysis, and release quality gating, reflecting a data-driven development approach. The codebase is well-structured with clear separation of concerns, extensive test coverage, and modular components that can be reused in other projects.
This repository implements a comprehensive toolkit for detecting and removing Google DeepMind's SynthID watermark from AI-generated images, primarily targeting Gemini models. It provides multiple bypass versions (V1–V4) that use pure signal-processing techniques—frequency-domain phase scrambling, JPEG degradation, elastic deformation, VAE round-tripping, and cross-color consensus codebook subtraction—to disrupt the embedded watermark while preserving visual fidelity. The codebase includes a drag-and-drop GUI, CLI tools, batch processing scripts, and a human-in-the-loop calibration workflow. It also contains extensive watermark investigation scripts that validated the watermark's presence in over 123,000 image pairs. While primarily an application (tool for watermark removal), it has a strong research character due to its analytical components and large-scale validation studies.
This repository implements a deep learning image inpainting model for watermark removal, based on the Contextual Attention (CVPR 2018) and Gated Convolution (ICCV 2019) papers. It provides a CLI tool (main.py) that loads a pre-trained TensorFlow 1.x checkpoint, applies a user-supplied mask (defaulting to a specific watermark pattern like iStock), and outputs the inpainted image. The code includes the full model definition (inpaint_model.py), custom operations (inpaint_ops.py), preprocessing utilities, batch inference scripts, and a Dockerfile. The architecture is a two-stage generator with a spectral normalization discriminator, trained on datasets like CelebA-HQ and Places2. However, the repository is primarily designed for inference; training code is present but not the primary focus. The project is heavily dependent on TensorFlow 1.15, the neuralgym library, and deprecated tf.contrib APIs, making it incompatible with modern TensorFlow 2.x without significant refactoring. The codebase is well-structured academically but not production-ready, with no tests and minimal documentation beyond the README.