The proposed deep learning-based watermarking method can be extended to handle multi-modal watermarks by incorporating additional neural network architectures specialized in processing audio and visual data. For audio elements, models like WaveNet or LSTM networks can be integrated to encode and decode audio information within the watermark. Similarly, for visual elements, architectures like CNNs or GANs can be utilized to embed and extract visual features seamlessly. By creating a unified framework that combines these different modalities, the model can learn to embed and extract multi-modal watermarks effectively. Additionally, attention mechanisms can be employed to focus on specific modalities during the watermarking process, ensuring that each type of information is encoded and decoded accurately.