A research team led by Dorjan Hitaj, Fabio De Gaspari, Flavio Corsetti, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, and Luigi V. Mancini has introduced TATTOOED, a white-box watermarking framework documented in Neural Computing and Applications, which embeds digital intellectual property signatures directly into model parameters to provide ownership verification and traitor tracing resistant to state-of-the-art watermark removal techniques. Traditional neural network watermarking schemes rely on backdooring specific trigger datasets or modifying statistical weight distributions through customized loss penalties during initial training, and have succumbed to laundering operations such as fine-tuning, weight pruning, and adversarial learning rate schedules. TATTOOED reimagines model parameter modification through Code Division Multiple Access spread-spectrum communication and Low-Density Parity-Check error correction codes, operating as a blind, multi-bit, post-training watermarking mechanism that treats the continuous real-valued weight tensor of a trained network as an additive communications channel subjected to Gaussian noise. The process begins with a 512-bit master secret key seeding pseudorandom number generators to produce orthogonal or quasi-orthogonal spreading codes, construct LDPC parity-check matrices, and select a targeted subset of weight indices. The payload is encoded using an LDPC matrix with a code rate of one-half with three ones per column, and a 200-bit pseudorandom preamble is prepended to the encoded payload to facilitate channel estimation during extraction.