constraints of neural networks. A DL model differs from a multimedia document in many fundamental ways. **Problem definition:** Designing an effective and efficient DNN watermarking scheme must meet the following six key criteria [14; 15; 16; 17; 18]:  
* Fidelity--the performance of the model should be well preserved after watermarking;
* Integrity--the watermarking scheme's ability to produce minimal false alarms and ensure that the watermarked model can be uniquely identified using the appropriate watermarking key;
* Robustness--the watermark cannot be easily removed when the model is attacked;
* Security--the watermark cannot be easily detected by malicious attackers;
* Capacity--the scheme's ability to effectively embed an amount of data (payload--size of the watermark) into the protected model;
* Efficiency--the computation cost of watermark embedding and extraction must be kept reasonably low. In practice, most existing schemes achieve only a partial compromise between these criteria. For instance, while the watermark should not degrade the model performance, it must simultaneously resist to attacks aimed at detecting or removing it. Among these attacks, the most common are the following:  
* The pruning attack (PA), where model weights whose absolute values are below a threshold are set to zero;
* The fine-tuning attack (FTA), which retrains and updates the model without decreasing its accuracy;
* The overwriting attack (OWA), where the attacker embeds their own watermark to suppress the original one;
* The Wang and Kerschbaum attack (WKA), which analyzes the weight distribution of the watermarked model to detect watermark presence;
* The Property Inference Attack (PIA), which trains a discriminator capable of differentiating watermarked from non-watermarked models by analyzing their statistical properties. Note that removal attacks are considered effective and efficient only if the watermark is completely removed while maintaining high test accuracy and requiring significantly fewer computational resources than retraining a model from scratch. Currently, the literature lacks both a formal unified definition of white-box watermarking schemes and a watermarking method that simultaneously satisfies all the requirements mentioned above. To address these gaps, we first present a formal unified framework for white-box watermarking algorithms. We then propose DICTION, a novel, dynamic white-box algorithm that overcomes the limitations of existing methods. In particular, we propose using a neural network following a generative adversarial-based strategy to map activation maps of the target model to the watermark space, rather than using a static projection matrix, as in DeepSigns. To embed a watermark \(b\) in a target model, DICTION first considers the initial portion of the target model up to layer \(l\) as a generative adversarial network (GAN) generator [19] and then trains a projection neural network (acting as a GAN discriminator) concurrently with the generator model. This joint training ensures that the trigger set activation maps of the watermarked model are mapped to the desired watermark \(b\), while those of the non-watermarked model (i.e., the original/initial one) are mapped to random watermarks. To detect the watermark, it is sufficient to use the trigger set samples as model input and verify that the projection neural network outputs the watermark from the corresponding activation maps. Using a neural network for mapping activation-maps-to-watermarking space, significantly expands the ability to embed robust watermarks while increasing the capacity. Additionally, our projection neural network can be trained either while embedding the watermark into a pretrained model or during initial training, whereas DeepSigns requires a trained target model. Another key innovation of our proposal is the use of a trigger set composed of samples from a latent space defined by its mean and standard deviation. DICATION can insert watermarks into the activation maps of a single layer or a combination of multiple layers.