## 2 Related Work
In [20], the authors proposed an audio blind watermarking algorithm called zero-watermarking seeking the audio frame suitable for the embedded watermark using scrambling encryption based on BTC, Arnold and chaos encryption. The proposed algorithm combines the zero-passing rate and short-time energy characteristics of the audio frame, the aim of the algorithm was to determine the watermarking embedding strength and the average energy of each segment. [PERSON] et al. [21] proposed a frequency domain transform model to be used as a blind watermarking technique, in this model the Artificial Bee Colony (ABC) was used for optimization purposes. The aim of the ABC technique was to find the best embedding factor for watermarking, this technique required only the watermarked image extraction, therefore it makes the proposed model a blind watermarking technique. The proposed model combined the Discrete Wavelet Transform (DWT) and the Discrete Cosine Transform (DCT), constructing a hybrid method for embedding and providing a better robustness scheme. Finally, the watermark encrypted image was embedded into the cover image. In [22] the authors proposed a watermarking framework based on Deep Neural Network (DNN), where the network outputs the image with a certain watermark. The proposed model validates the ownership of the image by identifying the network that generated the image. A secret key technique was used to control the watermark extraction to improve the security of the watermarking system; the authors proposed an additional step during the neural network training by adding noise to the generated image to resist attacks and improve robustness. [PERSON] et al. [23] proposed an improved encryption algorithm based on the Low-Density Prity Check Code (LDPC) by expanding the key space aiming to reduce the bit error rate and improve the security of the system. The proposed model shows that even if the encryption of the carrier image is illegally cracked, copyright protection still functions by extracting the watermark. In [24], the authors proposed double encryption using Arnold transform and Memristive Chaotic Oscillators (MCO) as digital watermarking technique. The proposed model used the Arnold Transform and the coefficients resulted from the MCO based on discrete cosine coefficient modification to embed the watermark based on fractal dimensions extracted using [PERSON]'s algorithm followed by the pixel transformation. The extreme learning machine was used as a semi-blind extraction of the watermark. [PERSON] et al. in [25] proposed a dual watermarking model for copyright protection by using the owner's fingerprint in addition to the signature as watermarks to validate the ownership and authority of the media. The authors proposed two different watermarks; if one is compromised the other one will be a safeguard to the media. For watermark embedding purposes, the coefficient selection procedure is modified in equal proportions in addition to using the DWT, DCT for the embedding block selection procedure. The proposed model operates only on tone/greyscale images and multitoned images. [PERSON] et al. [26] proposed a novel route to periodic and chaotic bursting by using the delayed pitchfork bifurcations around stable attractors to achieve different bursting dynamics by computing the corresponding characteristic polynomial. The proposed model uses these analytical results for bursting dynamics in the Lu system. This study aims to tackle the issue of complex bursting pattern, the proposed periodic or chaotic bursting oscillations show that the termination of bifurcation delay may lead the trajectory to switch to the different attractors. The authors of [27] used the bifurcation analytical approach to study the bursting dynamical properties of the van der Pol-Duffing system with the parametric delay feedback.