## 4 Organizational Experimental Setup
### Experimental Setup 3  
The third experimental setup utilizes \"ReLU\" activation for the hidden layer and \"sigmoid\" activation for the output layer. Both Alice and Bob employ a neural architecture that is defined by a \"concatenate and transform\" pattern. Initially, the plaintext message and the encryption key are concatenated. This merged information stream is then processed by a fully connected (FC) layer consisting of 64 units. Think of this layer as a complex combiner, allowing for potential interplay and integration between the message bits and the key bits. The activation function [34] utilized post this FC layer is the \"ReLU\", mathematically represented as shown in Equation (12):  
\[f(x)=\max\,(0,x) \tag{12}\]  
The ReLU function introduces non-linearity, ensuring the encrypted output is not just a straightforward combination of input bits. Following this is another FC layer tasked with outputting the encrypted or decrypted message. For this layer, the \"sigmoid\" activation function is harnessed. Mathematically, it is described as in Equation (13):  
\[f(x)=\frac{1}{1+e^{-x}} \tag{13}\]  
The choice of the sigmoid ensures that the output, whether encrypted or decrypted, remains constrained between 0 and 1, making it ideal for binary message representation. [PERSON]'s architecture mirrors that of [PERSON] and [PERSON], with a notable exception: [PERSON] lacks the key input. This deliberate omission means [PERSON] must decrypt messages without the corresponding key, a challenge that pushes the neural network to its limits. The entire system undergoes training for a staggering 500,000 iterations. Each training cycle involves generating random messages and keys [35]. [PERSON] then encrypts these messages, and both [PERSON] and [PERSON] make attempts at decryption. Their performance is gauged through loss functions, which measure the discrepancy between the original messages and their decrypted counterparts. Based on these losses, the weights in all the networks are adjusted using the Adam optimization algorithm [36]. The benchmark for success in this experimental setup is dual-faceted: [PERSON] and [PERSON] should be able to communicate with minimal decryption error, ensuring the integrity and secrecy of their conversation. In stark contrast, [PERSON]'s decryption attempts should yield a higher error, indicating the robustness of the encrypted communication against eavesdropping. Figure 11 shows that after an initial fluctuation with peaks reaching close to 19.77 s, the system's performance shows signs of stabilization within the 16 to 17 s range. As the system progresses further, there is a noticeable trend towards the lower end of the 16 s bracket, even dipping into the 15 s region. These data suggests that while there are sporadic spikes in execution time, the system predominantly operates with consistent efficiency. From Figures 12 and 13, upon analyzing the time metrics from the initial 500,000 steps of the training process, it is evident that the computation time exhibits quasi-stable behavior, oscillating around for an approximately mean duration of 16.51 s for every 2000 steps. Figure 11: Time series analysis for training during experimental setup 3. Figure 12: Reconstruction error of [PERSON] and [PERSON] during training during experimental setup 3. Figure 13: A line graph tracking the increasing cumulative time, in seconds, for every 2000 training steps up to 500,000, showcasing the escalating time commitment as training advances during experimental setup 3. After training our adversarial neural cryptography models over 500,000 steps, we've observed a consistent and significant improvement in secure communication between [PERSON] and [PERSON]. The loss values, a measure of the accuracy of the encryption and decryption process, have shown a remarkable reduction from initial values of around 0.0735 to 0.0147 for [PERSON]'s communications.