Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.

Importance: The transformative potential of artificial intelligence (AI), particularly via large language models, is increasingly being manifested in healthcare. Dietary interventions are foundational to weight management efforts, but whether AI techniques are presently capable of generating clinically applicable diet plans has not been evaluated.Objective: Our study sought to evaluate the potential of personalized AI-generated weight-loss diet plans for clinical applications by employing a survey-based assessment conducted by experts in the fields of obesity medicine and clinical nutrition.Design, setting, and participants: We utilized ChatGPT (4.0) to create weight-loss diet plans and selected two control diet plans from tertiary medical centers for comparison. Dietitians, physicians, and nurse practitioners specializing in obesity medicine or nutrition were invited to provide feedback on the AI-generated plans. Each plan was assessed blindly based on its effectiveness, balanced-ness, comprehensiveness, flexibility, and applicability. Personalized plans for hypothetical patients with specific health conditions were also evaluated.Main outcomes and measures: The primary outcomes measured included the indistinguishability of the AI diet plan from human-created plans, and the potential of personalized AI-generated diet plans for real-world clinical applications.Results: Of 95 participants, 67 completed the survey and were included in the final analysis. No significant differences were found among the three weight-loss diet plans in any evaluation category. Among the 14 experts who believed that they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized weight-loss diet plan was assessed, with scores above neutral for all evaluation variables. Several limitations, of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans.Conclusion: Despite certain limitations, our study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care.

In this paper, we propose a new hardware architecture implemented as a very large scaled integrated circuit by using an application-specific integrated circuit technology, where block-based calculations are used to generate holograms. The proposed hardware is structured to produce a part of a hologram in the block units in parallel. A block of a hologram is calculated using an object point, and then the calculation is repeated for all object points to obtain intermediate results that are accumulated to produce the final block of a hologram. This structure can be used to produce holograms of various sizes in real time with optimized memory access. The proposed hardware was implemented using the Hynix 0.18 μm CMOS technology of Magna Chip, Inc., and it has about 448 K gate counts and a silicon size of $3.592\mathrm{mm}\times 3.592\mathrm{mm}$. It can generate complex holograms and operate in a stable manner at a clock frequency of 200 MHz.

This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction, invisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques: finding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the location, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowest-frequency subbands, resulting from n-level Mallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking data and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark data into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the strength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various resolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks. With experimental results and comparison to the existing works we show that the proposed scheme has better performance than the previous works, except those which specialize in certain types of attacks.

In this paper we propose a hardware architecture for high-speed computer-generated hologram generation that significantly reduces the number of memory access times to avoid the bottleneck in the memory access operation. For this, we use three main schemes. The first is pixel-by-pixel calculation, rather than light source-by-source calculation. The second is a parallel calculation scheme extracted by modifying the previous recursive calculation scheme. The last scheme is a fully pipelined calculation scheme and exactly structured timing scheduling, achieved by adjusting the hardware. The proposed hardware is structured to calculate a row of a computer-generated hologram in parallel and each hologram pixel in a row is calculated independently. It consists of and input interface, an initial parameter calculator, hologram pixel calculators, a line buffer, and a memory controller. The implemented hardware to calculate a row of a $1920\times 1080$ computer-generated hologram in parallel uses 168,960 lookup tables, 153,944 registers, and 19,212 digital signal processing blocks in an Altera field programmable gate array environment. It can stably operate at 198 MHz. Because of three schemes, external memory bandwidth is reduced to approximately $1/20,000$ of the previous ones at the same calculation speed.