Search Results (495)

Robust watermarking has emerged as a pivotal field in digital security, particularly for protecting intellectual property. This study introduces a robust watermarking framework specifically engineered to withstand JPEG compression attacks. To evaluate our scheme, several performance metrics should be considered, including robustness against intentional JPEG compression, embedding capacity, and the perceptual quality of watermarked images. For practical applications, we choose the quick response code to serve as the watermark, and we extend our evaluation to high-resolution pictures, both captured by the authors and obtained on the Internet with no copyright issues. These images possess resolutions that are dozens-of-times higher than conventional test images, facilitating a vastly increased capacity. This expanded capacity makes the integration of visual secret sharing practical, adding an additional layer of cryptographic protection to the watermarking scheme. Simulation results present the effectiveness of our implementation, demonstrating that our approach achieves a superior balance between data integrity and visual fidelity, even when subjected to rigorous attack conditions. Full article

In a digital watermarking technology system, robustness, imperceptibility and payload capacity are the three core performance indicators that restrict one another. How to achieve the optimal balance between the three is still the key scientific challenge to be solved in this field. This paper proposes a digital watermarking algorithm based on the magnitude coefficient of Non-Subsampled Shearlet Transform Fast and Accurate Polar Harmonic Fourier Moments (NSST-FAPHFMs) and the Weibull–Power Cauchy (W-PC) statistical model. The algorithm consists of two stages: watermark embedding and detection. In the embedding phase, the original image is first decomposed by NSST multi-scale decomposition, and the high-frequency subbands are divided into non-overlapping blocks and partitioned. High-energy coefficient blocks are extracted to obtain NSST-FAPHFM magnitude coefficient features, which serve as robust carriers for watermark embedding. In the detection phase, the W-PC distribution is used to accurately statistically model the above magnitude coefficients to characterize their heavy-tailed characteristics and strong correlation structure. Maximum likelihood estimation (MLE) is employed to estimate the model parameters, and a blind watermark detection mechanism is further constructed by integrating the W-PC model with the Local Optimal Detector (LOD) under the Neyman–Pearson (N-P) criterion. Experimental results show that the proposed algorithm has good imperceptibility, and the area under the receiver operating characteristic curve (AUROC) can reach 0.9991 without attack. The algorithm maintains strong robustness against various attacks and can effectively realize the joint optimization of the three core performance indicators of the watermarking system. Full article

Digital watermarking is widely used to protect medical images in terms of ownership, authenticity, and traceability; however, the embedding process may introduce subtle modifications that can affect the reliability of deep-learning-based clinical analysis. Existing studies have shown that watermarking has a negligible effect on medical image classification; nevertheless, its impact on segmentation performance remains insufficiently explored. Therefore, this paper aims to investigate the effects of segmentation model enhancement on watermarked medical image analysis. In this context, three representative watermarking approaches were employed, and five baseline segmentation models, namely U-Net, ResUNet++, SegNet, FCDenseNet, and TernausNet, were evaluated on two benchmark datasets: LIDC-IDRI and BRISC. Additionally, a novel deep learning model with nested attention mechanisms was specifically designed to improve feature extraction and increase sensitivity to subtle pixel-level variations in watermarked images. Segmentation performance was assessed using five standard evaluation metrics, including mean Intersection over Union (mIoU), Dice Similarity Coefficient (DSC), and the 95th percentile Hausdorff Distance (HD95). The experimental results indicate consistently minor performance degradation across both datasets. For the BRISC dataset, the reduction in mIoU ranges from 0.15% to 0.44%, while for the LIDC-IDRI dataset, it ranges from 0.19% to 0.29% compared with the no-watermarking baseline. These findings provide quantitative insight into the compatibility of watermarking techniques for medical image protection with AI-based medical image segmentation systems, highlighting their potential for broader clinical application. Full article

Underwater acoustic (UWA) sensor and control links carry mostly short messages (below ∼10 kB) over time-varying, low-SNR multipath channels, which is a regime where forward error correction (FEC) operates on short, finite blocks where a single static configuration is inefficient. Adaptive schemes for these links typically adjust the modulation order and code rate; the payload segment (block) size—which, together with the code rate, sets the coded block length that governs the finite-blocklength penalty for short messages—is seldom adapted per transmission jointly with the choice of FEC scheme on a like-for-like footing. We propose a per-transmission controller that jointly selects the FEC scheme, code rate, and segment size from a prediction of the near-term channel state, which is paired with a like-for-like short-block benchmark of LDPC, list-decoded polar, BCH, Reed–Solomon, convolutional, and turbo codes. No single code dominates: under a unified ARQ goodput metric, the reliability–throughput frontier has a crossover that shifts with the channel, so the optimal FEC choice is channel-dependent. Across our experiments, the segment-size degree of freedom is the dominant throughput lever, capturing essentially all of the adaptation gain at low-to-mid SNR and over fading; switching the FEC family adds a further, bounded gain only where the frontier crosses between families (up to 10% at high-SNR AWGN, polar to RS). The joint controller essentially matches a fair single-family adaptive baseline off the crossover (to within a negligible prediction-overhead margin) and exceeds it at that crossover, beats a fixed, no-CSI code by up to 16%, and captures 91–99% of an oracle; a lightweight persistence predictor matches a learned LSTM for the first-order channel-state model studied. A statistics-driven replay using measured-channel parameters, and a recorded-channel replay over the public Watermark benchmark, preserve the same family ordering. Full article

Digital watermarking secures electroencephalogram (EEG) data in distributed Brain–Computer Interface (BCI) environments. However, existing single-domain deep learning schemes struggle to maintain robustness against clinical signal processing attacks due to EEG’s joint time–frequency nature. We introduce the Dual-Stream Frequency–Time Attention Network (DS-FTAN), utilizing an adaptive Spectral Gating Mechanism to embed information within robust, high-energy EEG spectral regions. A robustness simulation layer—encompassing resampling, spectral dropout, and band-pass filtering—is incorporated during training. Validations confirm DS-FTAN balances imperceptibility (PSNR > 36 dB) with reliable recovery. Specifically, it achieves >99.99% accuracy under no-attack conditions and maintains 86.52–98.77% accuracy across complex attacks (e.g., 50% cropping, band-pass filtering). This significantly outperforms time-domain baselines. Furthermore, DS-FTAN exhibits excellent zero-shot cross-channel generalization. It preserves diagnostic integrity, causing merely a 0.42% accuracy drop in downstream EEGNet intention recognition. Ultimately, this framework provides a reliable solution for privacy-preserving EEG data sharing. Full article

This article introduces the reconfigurable intelligent surface (RIS) to physical layer authentication (PLA) designs to explore the utility of RIS in both the radio frequency fingerprint (RFF)/channel fingerprint (CF)-based PLA technique and the tag embedding (TE)-based PLA technique. Two new PLA schemes are proposed, i.e., the controllable reflection-based PLA (CR-PLA) scheme and the watermark hopping-based PLA (WH-PLA) scheme, where the role of RIS is discussed and analyzed carefully. First of all, considering the performance of RFF/CF-based PLA technique is degraded by the inaccurate feature estimation, the CR-PLA scheme is proposed to improve the feature estimation accuracy and to amplify the estimation differences among multiple devices through reconfiguring the wireless propagation channel. Then, to improve the performance of the TE-based PLA technique and introduce it to the RIS-aided systems, the WH-PLA scheme is developed. This scheme adds the security information on the pilot signal or message signal alternatively for authentication according to a designed pseudorandom embedding sequence with high uncertainty and randomness. Our simulation results verify the better performance of the proposed schemes compared with the existing schemes. The challenges and open issues of PLA designs in the RIS-aided wireless communication systems are also presented. Full article

With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication and tamper detection framework (KPP-BA). This framework integrates key-dependent pixel permutation, hash-based message authentication code (HMAC)-SHA256 hash verification, and a parity-based 3-LSB minimal distortion embedding strategy. The core innovation lies in utilizing pseudo-random pixel permutation to disrupt spatial correlation within blocks, thereby effectively resisting collage and statistical analysis attacks. Furthermore, by combining the avalanche effect of HMAC-SHA256 with hybrid bit-plane feature extraction, the proposed method ensures extremely high sensitivity to subtle tampering. Experimental results on a dataset comprising 300 medical images demonstrate that the proposed method maintains superior visual quality while ensuring security, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 54.15 of 0.5 bit per pixel (bpp). Moreover, against various tampering attacks—including masking, copy–paste, circle masking, and collage—the method exhibits exceptional detection capabilities with an average detection accuracy of 99.99%. Compared with seven state-of-the-art methods, the proposed framework demonstrates significant advantages in both image fidelity and tamper localization precision, validating its feasibility and robustness for secure medical image transmission applications. Full article

The widespread deployment of photovoltaic (PV) inverters with digital control and communication systems has increased the power grid’s attack surface, making it more vulnerable to cyberattacks. This creates a need for locally implementable attack-detection methods that do not disrupt inverter operation. This paper therefore proposes an irradiance-driven natural watermarking approach for decentralized detection of false data injection (FDI) attacks on inverter terminal measurements. The approach leverages irradiance-driven DC-link voltage variations to watermark the inverter outputs, generating a non-removable signature in the true measurements. The proposed method is evaluated using a real-time hardware-in-the-loop model of a three-phase grid-following PV inverter that captures PV-array and grid-connection dynamics. Implementation robustness is further assessed on a separate hardware grid-forming inverter testbed with non-idealized components. In the tested cases, the detection model identifies noise-injection and replay attacks within $15\mathrm{ms}$, while otherwise undetectable model-based attacks are revealed when DC-link voltage variations between 5% and 10% occur. These experimental results demonstrate that irradiance-driven natural watermarking can reveal FDI attacks without affecting normal inverter operation. Full article

Recently, super-resolution transmission methods have been introduced on limited GPU. However, due to the stereotyped style exchange process, the GPU consumption and diversity of super-resolution style transfer are still challenging. In addition, existing methods can inadvertently lead to content leakage and uneven stroke size distribution, resulting in less attractive results. Hence, we introduce a fast and diverse super-resolution transfer (FDST) model, which can realize more flexible super-resolution multi-style transfer by mapping noise and information from another style in the style encoder. In addition, we propose two loss functions within the existing framework to support the preservation of content structure: Patch Content-Consistent Patch Loss (Patch-CCPL) and Patch contrastive loss. The proposed method effectively and elaborately integrates colors and texture structures. The key idea of FDST is to divide the super-resolution image into small patches, and then perform diverse style conversions on each small patch by injecting subtle noise or sub-style images. We implemented theoretical analyses and extensive results to qualitatively and quantitatively evaluate our method and compare it with the state-of-the-art algorithm. Extensive experiments on 4K content images demonstrate that FDST achieves a user preference score of 0.207, SSIM of 0.492, and LPIPS of 0.526, outperforming existing methods in content preservation while requiring only 2.573 GB model storage. Ablation studies confirm the contribution of each component, and a discussion of security applications including watermarking and forensic analysis is provided. Full article

Blind image watermarking models such as HiDDeN have laid an important foundation for end-to-end watermarking. Nevertheless, they still suffer from three major limitations: single-scale feature extraction, fixed fusion weights, and slow training convergence. To address these issues, this paper proposes an adaptive multi-scale watermarking algorithm based on collaborative dual-attention mechanisms. The algorithm designs an adaptive multi-scale feature fusion module (MA-FFM) with a dynamic gating network in the encoder, which flexibly combines local multi-scale textures with global contextual information, overcoming the limitation of fixed fusion weights. In the decoder, a multi-level channel attention module is embedded to strengthen the extraction of watermark signals. The two attention modules work synergistically: the encoder focuses on adaptive feature fusion while the decoder leverages channel attention to selectively enhance watermark-related features, forming a dual-attention synergy that balances robustness and imperceptibility. Moreover, the dynamic gating network adaptively adjusts the contribution of local versus global features via learnable weights, whose evolution from approximately 0.51 to about 0.89 improves model interpretability. Experiments are conducted on the COCO 2017 dataset. Compared with HiDDeN, the proposed algorithm reduces the bit error rate (BER) from 0.1696 to 0.1538 under no attack with a relative reduction of 9.3%, increases PSNR by 0.61 dB, and improves SSIM from 0.9058 to 0.9077. Under various attacks—including JPEG compression, Gaussian noise, salt-and-pepper noise, and brightness/contrast adjustments—the BER remains consistently lower than that of HiDDeN. Ablation studies confirm the effectiveness of each module. Overall, the proposed algorithm preserves visual quality, improves the accuracy of watermark embedding and extraction, and exhibits strong generalization robustness against common image distortions. Full article

3D Gaussian Splatting (3DGS) has emerged as a cornerstone technique for 3D asset acquisition. However, existing copyright protection methods for 3DGS predominantly rely on embedding watermarks directly into Gaussian primitives, which inevitably degrades rendering quality. To address this issue, this paper proposes a zero-watermarking framework. By directly mapping the inherent features of rendered images to copyright information without modifying Gaussian parameters, the framework achieves perfect visual fidelity. Conventional image zero-watermarking maps features of a single image to a dedicated watermark. In contrast, our method guarantees mapping consistency: features of rendered images from any unknown viewpoint can be mapped to the same copyright identifier. To address this cross-view consistency challenge, we introduce an uncertainty-guided strategy that scores individual pixels to guide the decoder to mine shared features across multiple perspectives. This strategy enables accurate watermark retrieval even from novel viewpoints. Extensive experiments on the Blender, LLFF, and MipNeRF-360 datasets demonstrate that our method achieves superior performance, characterized by high message capacity, strong adversarial robustness, and a low false positive rate (FPR), while fully maintaining the integrity of the original 3DGS model. Full article

Private cloud object stores provide infrastructure isolation but leave application-layer data exposed to insider threats and compromised credentials. This paper presents an engineering integration of an Add-Rotate-XOR (ARX) block cipher and multi-bit Least Significant Bit (LSB) steganography into an end-to-end pipeline for private MinIO object storage. The cipher, KREA v2, is a SPECK-64/128 derived ARX construction with three application-driven choices: CRC32 key whitening, byte-aligned rotations ($\alpha =7$, $\beta =2$), and deterministic CTR-mode nonces. Mixed Integer Linear Programming (MILP) trail analysis matches SPECK-64/128’s minimum-trail weights through rounds 1–4. KREA v2 ciphertext meets standard keystream-quality preconditions (NIST SP 800-22 battery, 49.98% mean avalanche, Shannon entropy 7.9992–7.9998 bits/byte across realistic XML, JSON, video, and HTTP/2 payloads). Modified LSB (MLSB) embeds 3 bits per RGB channel with an XOR watermark at 37–38 dB Peak Signal-to-Noise Ratio (PSNR), providing $3\times$ standard-LSB capacity. Steganalysis uses chi-square and RS detectors plus a Convolutional Neural Network (CNN) detector (Yedroudj-Net) trained on 8000 BOSSBase-1.01 cover/stego pairs; CNN area under the ROC curve is ≥0.999 against the watermarked variant. The MinIO pipeline runs at 355.1 ms (68.6% network I/O) with 100% message fidelity. The XOR watermark increases RS detectability above 75% capacity; a 200-image ablation cuts median RS detection (0.289 to 0.000) and mean (0.342 to 0.130) in a sparse-keystream variant, prioritised for follow-on full-scale evaluation. The architecture is offered as a documented engineering integration with explicit security caveats and threat-model boundaries, not as a production-hardened cryptographic primitive. Full article

To combat counterfeiting in down products and enhance enterprise credibility through technical means, this paper proposes a blockchain-based down quality traceability framework named DPT (down-product traceability). Built on Hyperledger Fabric, the framework integrates the InterPlanetary File System (IPFS), digital anti-counterfeiting watermarks (DW), QR codes, and category-specific encryption strategies to establish a trusted data chain throughout the entire supply chain. Role-based access control (RBAC) is adopted to ensure the secure submission and query of traceability information by all supply chain participants. A trinity data storage architecture is designed to achieve secure and efficient data management. A full-fledged application system was developed and deployed in cooperation with a leading down products enterprise to validate its practical applicability. Performance evaluation using Hyperledger Caliper 0.6.0, which focuses on throughput, latency, and resource utilization under stress testing, confirms that the DPT framework meets the requirements of real-world production. Furthermore, practical sales data verify that the proposed system effectively enhances consumer trust and mitigates counterfeiting behaviors in the market. Future work will focus on further optimizing write operation performance and evolving the system into a more robust clustered architecture. Full article

Long propagation delays, severe multipaths, and narrow bandwidths make feedback-based link adaptation impractical in UWA channels at kilometer ranges, so we replace the feedback step with a prediction step. The transmitter runs a two-layer coded OFDM link in which Polar codes handle bit errors, and Raptor fountain codes handle packet erasures, with the Raptor overhead (OH) as the only real-time knob. The OH is picked from a lookup table indexed by three quantities the receiver can estimate online: SNR, RMS delay spread, and Doppler frequency. Two CSI predictors feed that table: Temporal Multiple Sparse Bayesian Learning (TMSBL), which exploits delay-domain sparsity, and the Square-Root Unscented Kalman Filter (SRUKF), which tracks per-subcarrier variations. We evaluate the system in five channel environments (AWGN, Rayleigh, K-distribution, Bellhop ray-tracing, and synthetic proxies parameterized from the KAM11 and WATERMARK sea-trial statistics). Across the nine Bellhop scenarios, the adaptive link’s throughput gain over a fixed-OH ($\mathrm{OH}=1.5$) baseline at SNR $=4$ dB spans roughly $-4\%$ to $+30\%$, with the largest benefit in the marginal short-range cases (shallow 500 m, $+30\%$) where the fixed baseline is most over-provisioned and near-parity elsewhere. The scheme’s principal benefit is collapse prevention, tracking the Oracle within the safety margin and avoiding the throughput collapse the fixed baseline suffers at low SNRs. This effect is specific to the physically structured Bellhop channels; in the homogeneous Rayleigh and K-distribution channels, both schemes enter deep outage at very low SNRs, so it is not a universal guarantee. A 1000-trial high-resolution Rayleigh campaign sharpens the head-to-head between predictors: at SNR $=4$ dB, SRUKF + OH reaches PER $0.048$ (95% Wilson CI $[0.036, 0.063]$) and TMSBL + OH reaches $0.071$ ($[0.057, 0.089]$), and at SNR $=12$ dB, their throughputs ($0.748$ and $0.746$) are statistically indistinguishable from each other (95% Wilson halfwidth $\sim \pm$0.014) and lie close to the Oracle’s $0.768$ (within $\sim 0.02$). The two predictors therefore occupy overlapping operating regions once the safety margin is matched, and a sparsity-dependent tendency (TMSBL in sparse multipath, SRUKF in dense multipath) appears only in physically structured channels and only at the $n=100$ screening level, where it is not statistically resolved and would benefit from higher-trial confirmation. A finite-blocklength check confirms that CA-SCL-decoded Polar codes at $N=128$ stay within $0.5$ dB of the Polyanskiy normal approximation, which makes Polar a sensible inner code at UWA block lengths. Full article

The authentication and protection of deep neural network models have become challenging due to their widespread distribution and reuse, making them vulnerable to unauthorized access. This paper addresses the need for ownership verification by proposing a hybrid neural network watermarking method for secure model authentication. The approach combines a steganographic watermark embedded into stable model weights with a user code for watermark recovery encoded in auxiliary hidden layers. Stable parameters are identified through a reduced training to estimate gradient variations for the watermark insertion with minimal impact on model performance. Additionally, two auxiliary layers are introduced, to store in the first layer the metadata indices from the selected weights where the watermark was embedded and in the second layer the user code, supporting secure identification and verification. Experimental evaluations demonstrate that the proposed method remains robust under different model optimization attacks, including pruning, fine-tuning, additive noise injection, and parameter overwriting, while preserving model performance. The proposed framework achieves a BER = 0 under several moderate attack scenarios across different neural network models, whereas more aggressive optimizations degrade the watermark recovery performance. These results indicate that the proposed framework provides an effective solution for neural network ownership protection while maintaining the model performance. Full article