# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 2 Related Works  
### Deep Learning in Data Hiding  
In recent years, deep learning methods have become the dominant paradigm in information hiding [14], encompassing steganography and watermarking. The key advantage of neural network-based approaches is their ability to automatically adapt the signature's embedding process to the image's characteristics, as well as their resilience to various distortions and transformations. This robustness can be achieved by appropriately selecting training data and augmentation conditions. One of the first widely cited works in this field is HiDDeN [15], which introduced an end-to-end approach based on convolutional networks for embedding and recovering messages from images without requiring access to the original during decoding. A particularly valuable aspect of this approach is the inclusion of transmission-related distortions during training by incorporating a dedicated noise simulation block between the encoder and decoder. This allows the model to learn how to handle various distortions, significantly improving its usability in real-world applications. Since then, numerous variations and improvements have emerged. One example is the Distortion Agnostic Model [16], which demonstrated more effective performance in scenarios where distortions were absent in the training dataset. Other studies have expanded the scope of application: [PERSON] et al. introduced StegaStamp [17]. This method enables embedding hyperlinks into photographs in a way that allows retrieval even after printing and capturing them with a camera. An interesting direction in developing data-hiding technologies involves architectures where both the hiding and retrieval processes are based on the same models. This concept was presented in HiNet [18], which employs an invertible neural network (INN), allowing the data-hiding process (forward pass) and the retrieval process (backward pass) to share the same parameters. This architecture eliminates the need for separate networks for both tasks, reducing the risk of errors caused by inconsistencies between models. Additionally, HiNet integrates a discrete wavelet transform (DWT) to embed information efficiently into the high-frequency components of an image. This improves the effectiveness of data hiding and enhances resistance to detection using steganalysis tools. Another crucial aspect of HiNet is its loss function based on low-frequency components, ensuring a higher quality and security in information embedding. In the context of potential risks associated with model leakage, the literature proposes techniques incorporating an additional encryption key into the data-hiding process, addressing the issue of the compromise of the encoding process. One such technique is DKiS [19], which introduces a private key into the secret pipeline, using it for data permutation and multiplication operations within the model. As a result, even if the algorithm is publicly disclosed, access to the hidden information remains impossible without knowledge of the correct key. Encryption keys can also be used in traditional architectures as an additional security layer, protecting against unauthorized access to encoded data within an image. The primary objective in training watermarking algorithms is the optimization of two key metrics: maintaining the high transparency of the host object in which the hidden information is embedded and ensuring the accurate extraction of the encoded data. This process is achieved through loss functions, which balance these requirements. In addition to commonly used loss functions such as Mean Squared Error (MSE) and Mean Absolute Error (MAE), more perceptually oriented metrics are gaining importance, including the Structural Similarity Index (SSIM) [20] and Learned Perceptual Image Patch Similarity (LPIPS) [21]. These functions are beneficial as they account for subtle differences perceived by the human eye, minimizing visible artifacts in the encoded image.