Search Results (397)

We present a path-based curvature representation of the gravitational bending of light in black-hole (BH) spacetimes. The bending angle is written as a one-dimensional line integral of the optical Gaussian curvature ${\mathcal{K}}_{\mathrm{opt}}$ along the photon trajectory, weighted by a geometric kernel $W(r,b)$. This representation sits within the Gibbons–Werner Gauss–Bonnet (GB) optical-geometry family rather than alongside it: the kernel is fixed by a co-area reduction of the GB surface integral along an undeflected reference path, and the single new computational object is the resulting radial integral together with its cumulative, directly plottable reading of how the deflection builds up along the ray. With the lever-arm choice $W=\sqrt{r^2-b^2}$, the integral reproduces $\hat{\alpha }=4M/b$ for every static, asymptotically flat metric (Theorem 1) and evaluates in closed form for Schwarzschild, Reissner–Nordström (RN), and equatorial Kerr. The representation becomes reliable at a large impact parameter; at the small impact parameters relevant to horizon-scale imaging, it is not numerically competitive with the standard expansions, a limitation we quantify. Beyond leading order the kernel must import information from the bent geodesic, after which the scheme reconstructs the known perturbative series; the second-order mismatch in the lever-arm result therefore measures, rather than hides, the deformation of the photon path away from the straight-line reference. Finite source–observer distances enter through the Ono–Ishihara–Asada (OIA) construction, and a winding-sum continuation outlines the route toward the strong-deflection regime, whose closed-form reduction is left to future work. Full article

This study investigates the composition, morphology and cultural significance of red pigment traces identified on bone pointed tools discovered in the Chalcolithic tell settlement of Pietrele–Măgura Gorgana, attributed to the Kodjadermen–Gumelnița–Karanovo VI cultural complex (4600–4250 BC). The observed use-wear patterns are consistent with repeated contact with soft, non-abrasive materials, including hide working, pigment application on leather or other organic surfaces, fiber manipulation, and perforation of soft substrates. Use-wear analysis revealed polished and flattened distal ends, compatible with repeated use on soft, non-abrasive materials, such as hide, leather, fiber, or other organic substrates. The possibility of pigment application directly on skin, in a practice analogous to tattooing, as previously published, cannot be excluded but remains speculative in the absence of experimental reference data or residue evidence specifically linked to such use. An associated ceramic container was tentatively interpreted as a possible vessel for ochre preparation, suggesting local processing of the pigment. The artifacts were investigated using multi-analytical archaeometric methods: SEM-EDS, AFM, TEM, FTIR, Raman, TGA, CLSM and pseudo-color image segmentation and 3D rendering of porosity distribution. The results consistently identified an iron oxide-based pigment, dominated by hematite and/or goethite, specific to ochre. Pigment particles (50–300 nm) form a well-defined superficial layer on the bone substrate, without Fe–Ca reactions at the interface. The simultaneous presence of Ca, P, Si, Mg and K indicates a silicate matrix with an apatite component, compatible with local and poorly purified raw materials. CIELAB colorimetric analyses revealed significant chromatic variability, suggesting the use of hematite-rich pigments and possible thermal transformations of goethite. The results contribute to the understanding of the pigment technologies of the Chalcolithic communities of the Lower Danube. Full article

In this paper, we propose a block-distortion-free reversible data hiding method for encryption-then-compression (EtC) images that supports fully flexible access privileges without constraints on the restoration order. The proposed approach redesigns the pre-processing strategy of previous work to ensure a clear separation of processing roles between the image owner and the data hider. It also introduces a pixel-value modification process that divides the target range into two regions to mitigate the influence of negative–positive inversion during restoration. As a result, block distortion in marked images is eliminated while preserving role separation between the image owner and the data hider. The proposed method offers four key advantages: flexible access privileges, elimination of block distortion, explicit role separation, and competitive hiding capacity comparable to existing methods with flexible restoration capabilities. Experimental results demonstrate that the proposed method achieves a high marked-image quality and competitive hiding capacity while maintaining the compression performance of marked EtC images. Furthermore, security analysis confirms the robustness of the generated EtC images against a representative ciphertext-only attack. Full article

Problem: In the deep image steganography field, the main challenge is to achieve a balance between visual image quality, reliably recovering the message, robustness, and interpretability, especially regarding image distortion because of noise, attack, resizing, and cropping. Solution: In this paper, we propose to combine deterministic pattern-based embedding with a deep neural refinement network to achieve a strong balance between robustness, simplicity, and quality. Methodology: First of all, we embed binary messages using spatial patterns, then refine the stego image, using an encoder–decoder network and enhanced with an attention mechanism. Results: The experimental results record PSNR values between 34.9 and 37.8 dB and SSIM values above 0.99, with zero BER under no-attack, noise, and resizing conditions. Moderate degradation is observed under blur (BER ≈ 0.125), while cropping significantly affects performance (BER ≈ 0.575). Contribution: The proposed approach introduces an interpretable and stable hybrid design that effectively balances imperceptibility and robustness, while maintaining reliable message recovery in practical scenarios. The use of differentiable attacks through training enhances robustness against common distortions such as noise, blur, and resizing. Full article

At present, there are no easily understood explainable artificial intelligence (AI) methods for discrete token inputs, like text. Most explainable AI techniques do not extend well to token sequences, where both local and global features matter, because state-of-the-art models, like transformers, tend to focus on global connections. Therefore, existing explainable AI algorithms fail by (i) identifying disparate tokens of importance, or (ii) assigning a large number of tokens a low value of importance. This method for explainable AI for tokens-based classifiers generalizes a mask-based explainable AI algorithm designed originally for images. It starts with an Explainer neural network that is trained to create masks to hide information not relevant for classification. Then, the Hadamard product of the mask and the continuous values of the classifier’s embedding layer is taken and passed through the classifier, changing the magnitude of the embedding vector but keeping the orientation unchanged. The Explainer is trained for a taxonomic classifier for nucleotide sequences and it is shown that the masked segments are less relevant to classification than the unmasked ones. This method focused on the importance the token as a whole (i.e., a segment of the input sequence), producing a human-readable explanation. Full article

With the rapid advancement of the Internet of Medical Things (IoMT), the efficient transmission and management of large-scale medical images in bandwidth- and resource-constrained networks remain critical challenges. This paper proposes a high-payload data hiding method in Absolute Moment Block Truncation Coding (AMBTC)-compressed medical images based on block classification. Image blocks are categorized into flat, smooth, and complex types according to the difference between high and low values, and adaptive embedding and extraction strategies are applied to each type. The proposed method integrates secret data into the compression framework, thereby enhancing efficiency while maintaining visual quality. Experimental results demonstrate an average efficiency of 59% and an average PSNR of approximately 30 dB. Furthermore, visual and structural evaluations indicate that the proposed method effectively preserves textures and boundaries. These results confirm the feasibility of integrating high-payload data hiding into AMBTC compression for efficient medical image storage and transmission in IoMT environments. Full article

Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data. RDH relies on accurate prediction of pixels to generate sharply distributed prediction error histograms, thereby maximizing embedding capacity and minimizing visual distortion. While convolutional neural network (CNN)-based predictors excel in smooth regions of cover images by leveraging local correlation, they often fail to produce accurate predictions in the textured regions. To address the limitation of CNN predictors, we propose a novel attention fusion-based CNN predictor (AFCNNP) that adaptively combines the CNN predictor with a non-local means (NLM) predictor. The proposed fusion framework learns spatial weight maps to favor CNN predictions in smooth regions and NLM predictions in textured regions. The experimental results show that the proposed framework outperforms other state-of-the-art CNN predictors by significantly lowering the mean absolute error, mean squared error, and variance of prediction errors, leading to more accurate pixel predictions. With the proposed fusion framework, the embedding and visual performance of prediction error expansion (PEE)-based RDH is improved compared to typical CNN-based RDH methods. Full article

This paper aims to secure sensitive data generated by IoT devices by introducing a lightweight hybrid approach that combines steganography and cryptography. While classical cryptography offers confidentiality guarantees, the visibility of the produced ciphertexts keeps them at risk of traffic analysis, which could reveal communication patterns. Although some studies use Curve25519-based protocols, ECC paired with RDWT, or VLSB-based steganography, there is no complete approach that combines cryptographic and steganographic methods that is tailored to IoT devices. Our proposed scheme addresses this gap by integrating X25519 with Elligator 2 for efficient key exchange, using Ascon-AEAD128 for encryption, and finally hiding the encrypted payload within cover images using hybrid DWT-DCT steganography. When compared to similar hybrid approaches, our method achieves better performance, with results showing high imperceptibility, low computational overhead, and good resistance to noise. The cryptographic-steganographic combo adopted by our proposed framework improves confidentiality, integrity, and resistance to detection in resource-constrained IoT systems. Full article

Reversible data hiding (RDH) is a key technique in secure multimedia applications, enabling the exact recovery of both embedded data and the original cover content. To further enhance security and embedding capacity, this paper proposes a dual-image reversible data hiding (DIRDH) method based on absolute moment block truncation coding (AMBTC). In the proposed scheme, two identical AMBTC-decoded images are exploited as twin covers, and secret bits are adaptively embedded into paired pixels using a variable embedding rate. To ensure data integrity, a lightweight Hash-based Message Authentication Code (HMAC) mechanism is integrated, allowing reliable detection of tampering without additional side information. Experimental results demonstrate that the proposed method achieves high embedding capacity while preserving good visual quality and provides effective authentication against representative tampering cases, including pixel modification, noise addition, and cropping. These contributions highlight the advantages of combining DIRDH with AMBTC, offering a practical and secure solution for high-capacity reversible data hiding. Full article

To address the issues of insufficient restoration of texture details in deblurred images and inadequate learning of frequency domain features, an image deblurring algorithm based on frequency domain feature enhancement and convolutional neural networks is proposed. In this architecture, firstly, a Fourier residual module with a parallel structure is constructed to achieve collaborative learning and modeling of spatial and frequency domain features, aiming to improve frequency domain feature learning capability and the restoration effect of the texture details; secondly, a gated controlled feed-forward unit acts on the Fourier residual module to further enhance the nonlinear expression ability of the algorithm; thirdly, a supervised attention module is improved and added to the decoder to promote more effective capture of key features for image reconstruction; finally, the weighted sum of spatial domain Charbonnier loss function and frequency domain loss function is defined as a novel total loss function. In addition, to verify the performance of our proposed algorithm, we conducted experiments on the GOPRO and HIDE datasets. Through experiments on the GOPRO, we obtained an SSIM and an LPIPS of 0.961 and 0.0278, respectively. With regard to the experiments on the HIDE datasets, we obtained an SSIM and an LPIPS of 0.941 and 0.0286, respectively. As for parameter count and running time, their values were 1.197 and 9.15 × 10⁶, respectively, obtained by the experiments on the GOPRO. In all algorithms, the values of our proposed algorithm are optimal. However, the PSNR of our proposed algorithm is very close to that of the latest comparison algorithm and is suboptimal. In a word, experimental results have demonstrated that our proposed algorithm effectively removes blur while better preserving the details and edges of the image. Therefore, it has more practical value and prospects in computer vision tasks. Full article

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security. Full article

Generative image steganography is a key technology for secure information transmission, but existing deep learning-based generative steganographic methods suffer from an extremely high bit error rate (BER) and degraded steganographic image quality in low-bit-rate embedding tasks in which secret information needs duplication or padding to match the model input size. In addition, it is difficult to balance BER reduction and imperceptibility of stego-images. To address these issues, this paper proposes a novel generative image steganography algorithm based on flip watermarking, with the core novelty of designing a mirror flipping preprocessing mechanism to achieve a redundant watermark and eliminate information errors caused by duplication or padding, and constructing an end-to-end Mini-Hide steganographic framework to integrate flip watermarking with generative steganography for the first time. Specifically, the proposed method first converts the binary bitstream of secret information into a square matrix, and performs vertical, horizontal and vertical–horizontal mirror flipping on the matrix to form a redundant basic watermark, which is then expanded to a secret image with the same size as the cover image. After that, the secret image is preprocessed by a preparation network and then input into an encoding network together with the cover image to generate a stego-image. Finally, the generated stego-image is input into the decoding network to extract the secret image. Subsequently, the inverse operation of flip watermarking is performed on the extracted secret image to recover the original binary bitstream. Extensive experiments are conducted on the public COCO dataset ($256\times 256$ pixels) with BER, PSNR, and SSIM, and the proposed method is compared with state-of-the-art generative steganographic methods. Quantitative results show that the proposed method achieves a $0\%$ BER for secret information of $8\times 8$ to $64\times 64$ bits, and the BER is only $0.00002\%$ for $256\times 256$-bit secret information; the PSNR of stego-images reaches 37.75 dB, and the SSIM hits 0.96, which are 7.07 dB and 0.02 higher than those of the classic HiDDeN method ($64\times 64$ bit) respectively. We also validated the flip watermark module by integrating into other methods; the results also show that the PSNR of FNNS-D is improved by 13.12 dB ($256\times 256$), and the BER of SteganoGAN is reduced by $99.99\%$ ($256\times 256$ bit). In addition, the proposed method breaks the embedding size limit of HiDDeN (≤$64\times 64$ bit) and supports up to $256\times 256$-bit secret information embedding with stable performance. This work significantly reduces the BER of generative image steganography while improving the visual quality of stego-images, provides a new preprocessing and optimization scheme for low-BER generative steganographic algorithm design, and also offers a universal lightweight module for performance improvement of existing steganographic methods, which has important theoretical and practical significance for enhancing the security and reliability of covert information transmission in the field of information security. Full article

The rapid growth of digital communication has heightened the need for the secure transfer of sensitive image data. This is due to the increasing threats posed by cyberattacks and unauthorized access. Traditional encryption methods, while effective for text and binary data, often face significant challenges when applied to images, due to their larger size and complex structure. These characteristics make it difficult to provide a robust security solution. In this paper, we present a fast and efficient hybrid image encryption and steganography algorithm that leverages a substitution–permutation network (SPN), a chaotic logistic map (CLM), and least-significant-bit (LSB) substitution. This approach aims to improve data security and confidentiality while maintaining low computational complexity. The chaotic map generates random sequences for substitution and permutation, ensuring high unpredictability. The SPN framework improves the confusion and diffusion properties of the encryption process. The LSB substitution method hides the encrypted data values within the pixels of the cover image. We evaluate the security and efficiency of the proposed algorithm using various statistical tests, including measurement of the mean square error (MSE) and peak signal-to-noise ratio (PSNR) and pixel difference histogram (PDH) analysis. The results indicate that our algorithm outperforms many existing methods in terms of speed and efficiency, making it suitable for real-time hybrid encryption and steganography applications. Full article

We developed a novel pattern-based dual-image reversible data hiding scheme utilizing vertical bars and bitten square blocks to improve data capacity, security, embedding efficiency, and visual quality. By embedding secret messages into two grayscale images that closely match the original, the approach significantly lowers the risk of detection by unauthorized parties. The technique includes reference matrix construction, position-based embedding, and perfect message extraction and image recovery. Experimental results demonstrate that the pattern-based method achieves a superior balance between payload and visual quality, making it well-suited for secure and high-performance applications. Full article

Encryption-then-compression (EtC) based on block scrambling enables privacy-preserving image sharing while maintaining compatibility with standard image codecs, yet it disrupts the spatial correlations and synchronization cues required by conventional reversible data hiding (RDH). This difficulty is further amplified in grayscale-based EtC pipelines, where RGB-to-YCbCr conversion and component serialization introduce representation shifts and non-bijective rounding/clamping effects, complicating reliable embedding and extraction. This paper presents a unified RDH framework compatible with both RGB-based and grayscale-based block-scrambling EtC systems, without altering the underlying encryption procedures. The core idea is to restore embedding and extraction synchronization directly in the encrypted domain using two encryption-invariant cues: diagonal pixel absolute difference (DPAD) and an encryption-invariant synchronization index (EISI), together with domain-consistent handling of the grayscale conversion pipeline. Experimental results on standard datasets demonstrate perfect reversibility and stable embedding performance under the evaluated settings, with negligible impact on lossless compressibility. We further observe that the proposed embedding can increase statistical dispersion within encrypted blocks; although not designed as a security enhancement, this effect degrades the performance of representative texture-based analyses in the considered ciphertext-only setting. Full article