## 2 Overview of Related Works
They trained an end-to-end pair of Convolutional Neural Networks (CNNs) for encoding and decoding to create stego images from cover images and black-and-white secret images, and to recover the secret images from the stego images. Experimental results indicated a high PSNR value (>29 dB). However, the absence of a stego key (key for the stego image) poses a risk of secret information leakage. If the decryption network is compromised or if the structure of the decryption network is revealed, the encoding and decoding pair becomes unusable, necessitating a retraining from scratch with different network structures. [PERSON] and colleagues [42] developed the DeepKeyStego technique based on deep networks. They successfully trained and deployed DeepKeyStego for both symmetric and asymmetric steganography. In the field of wireless networks, in the context of MRCnetworks, [PERSON] and colleagues [43] introduced a deep reinforcement learning algorithm known as GPDS. GPDS has the ability to enhance the Signal-to-Interference-plus-Noise Ratio (SINR) protection level and channel throughput for mobile devices in uncertain and dynamic communication environments. Experimental results have shown that the proposed GPDS achieves superior noise resistance effectiveness compared to state-of-the-art algorithms. [PERSON] and their team [44] have also proposed a trustworthy deep reinforcement learning approach for evaluating the reliability of devices in vehicular networks. This approach includes our intelligent trustworthy system model and the DPGAQ scheme to counteract spoofing attacks from malicious vehicles. [PERSON] and colleagues, on the other hand, presented the Adaptive Packet Minimizing Delay (APMD) protocol [45]. APMD is designed to reduce latency and ensure reliability in industrial Wireless Sensor Networks (WSN). [PERSON] and their colleagues [46] have proposed a method to evaluate the trustworthiness of associative learning in the context of Digital Twin for Mobile Networks (DTMN). The proposed framework can be employed to address the issue of selecting reliable users in the associative learning scenario, with the goal of enhancing the security of associative learning and ensuring the performance of DTMN. [PERSON] and their team [47] introduced a novel Trust Layer-based Emergency Message Dissemination Model (TCEMD) within vehicular ad hoc networks (VANETs). In the proposed model, when emergency events occur, emergency messages can be disseminated among nearby vehicles through layered trust mechanisms. Within vehicular networks, [PERSON] and their colleagues [48] have put forth a plan known as BTMPP to provide a viable solution for balancing trust management and privacy protection within the vehicular network. The authors utilize Privacy-preserving Set Intersection (PSI) technology based on the well-known Bloom Filter (BF). [PERSON] [49] also successfully embedded a color image (confidential information) into a color cover image. Another approach of steganography involves methods to hide confidential information within network packets. [PERSON] and colleagues [50] proposed a hidden code technique based on the length of the user data field in packets with random sizes such as UDP, utilizing the parity of the user data field length to represent binary information. However, it is not difficult for third parties to extract the hidden information. [PERSON] and [PERSON] [51] developed a method to embed confidential information into the overflow field of the timestamp option in an IPv4 network packet. This method does not trigger any alerts in network analysis applications like Wireshark and allows embedding 20 bits per IPv4 network packet. [PERSON] and [PERSON] [52] encrypted confidential information using RSA before embedding it into the flow label field in the IPv6 Header, enabling embedding 20 bits per IPv6 network packet. [PERSON] and colleagues [53] utilized DNS messages to hide confidential information.