The team, led by Abhijit Patil of K J Somaiya Institute of Technology in Mumbai, together with Ramesh Shelke of S. S. Jondhale College of Engineering and Dilendra Hiran of Pacific Academy of Higher Education and Research University, set out to fix a specific weakness in earlier watermarking research. Many existing schemes process audio in overlapping segments when embedding a watermark, which drives up computational cost without necessarily improving security. Overlapping windows are a standard tool in signal processing because they smooth transitions between frames, but in the watermarking context the authors argue that the redundancy they introduce is largely wasted effort. Their system instead guarantees non-overlapping segmentation from the very first stage, trimming the computational overhead that has plagued previous approaches.

The pipeline begins with a technique borrowed from classical spectral analysis: the FlatTop window. Windowing functions are mathematical tapers applied to short stretches of a signal before further processing, and each shape trades off different properties. FlatTop windows are prized for their exceptionally flat passband, which means they measure the amplitude of frequency components with minimal distortion, at the cost of a wider main lobe. In RAWS-SBS, the FlatTop window slices an incoming audio signal into frames that do not overlap, establishing a clean, computationally frugal foundation for everything that follows. This choice matters because every subsequent operation, from transform to embedding to extraction, operates on these frames, so inefficiency at the segmentation stage multiplies through the whole system.

Once segmented, the frames undergo a decomposition step using what the authors term a Polar Cartesian-based Discrete Wavelet Transform, or PC-DWT. The discrete wavelet transform is a workhorse of modern signal processing, breaking a signal into components that capture both frequency content and where in time that content occurs, unlike the plain Fourier transform, which discards timing information. The polar Cartesian variant reformulates this decomposition, and the transformed coefficients are then arranged into matrices from which features are extracted. These features feed the next critical decision: where, exactly, inside the audio should the watermark bits live? Placing a watermark carelessly makes it audible to listeners or fragile under compression and filtering; placing it too timidly makes it easy to destroy.

To answer that placement question, the researchers developed an optimization method they call Gamma Functional Energy Valley Optimization, or GFEVO. Optimization algorithms of this family search a large space of candidate solutions for the one that best balances competing objectives, and here the objective is finding embedding locations that maximize robustness while preserving perceptual quality. The reported results suggest the search paid off: the GFEVO stage achieved a Perceptual Evaluation of Speech Quality score of 4.256, a metric on a scale where values near 4.5 indicate quality nearly indistinguishable from the original. In practical terms, a listener hearing the watermarked audio would be very hard pressed to notice that anything hidden inside it at all.

Security enters the pipeline through the most colorful component of the system: Blancmange Curve Cryptography. The Blancmange curve, also known as the Takagi curve, is a fractal defined as an infinite sum of sawtooth-like functions. It is famous in mathematics for being continuous everywhere yet differentiable nowhere, a jagged, self-similar shape that never smooths out no matter how closely you zoom in. The authors harness this fractal’s irregular, deterministic complexity to encrypt the watermark image before embedding, converting the encrypted bits into a vector bit stream. An attacker who extracts the embedded payload without the key would face data scrambled according to the curve’s fractal arithmetic, adding a cryptographic layer on top of the steganographic one. It is a striking example of pure mathematics, a curve named after a wobbly pudding, finding duty in applied security engineering.

Embedding, however, is only half the problem. A watermark is useless if the legitimate owner cannot reliably pull it back out of an audio file that may have been compressed, filtered, resampled, or otherwise degraded on its journey through the digital world. For extraction, the team deployed a deep convolutional neural network with two distinctive design elements. The first is a spatial-and-cross architecture, which lets the network correlate information across different spatial regions of the transformed audio representation rather than treating each location in isolation. The second is a Scaled Gaussian Error Linear Unit, or SGELU, an activation function that governs how signals pass between network layers. Activation functions shape what a neural network can learn, and the Gaussian-error variant blends a smooth probabilistic weighting into the familiar rectified linear unit, potentially giving the extractor a richer response to the subtle statistical traces a watermark leaves behind.

The experimental numbers are where the system makes its case. The proposed method achieved a minimum Bit Error Rate of 0.03232, meaning that when the hidden watermark was extracted, fewer than four bits in every hundred came back wrong, a low error rate that translates directly into more reliable ownership verification. On overall watermarking accuracy, the system reached 97 percent. The authors benchmarked this against several established deep learning baselines, reporting improvements of 3.2 percent over a standard DCNN, 4.3 percent over a conventional CNN, 7.8 percent over a deep belief network, and 11.5 percent over a plain deep neural network. Those margins may look incremental, but in a field where a few percentage points of accuracy can determine whether a copyright claim holds up, they are meaningful.

Context helps explain why this line of research is heating up. Audio watermarking has moved far beyond its original role of tagging music files for royalty tracking. Recent work in the field targets desynchronization-resilient schemes that survive time-stretching attacks, blind quantum watermarking, neural approaches with invertible dual embedding, and systems designed specifically to detect deepfake audio proactively. As generative models make synthetic speech and music indistinguishable from human-created recordings, the ability to embed a verifiable, tamper-evident signature inside audio is becoming a frontline defense. Watermarks that survive aggressive processing while remaining inaudible are the technical backbone for proving that a clip is authentic, or flagging one that is not.

The RAWS-SBS study, received by the journal in 2023 and published on 31 August 2026 after an unusually long revision cycle, reflects that broader urgency. Its authors report no funding source and no competing interests, and they state that the data generated in the study are available from the corresponding author on reasonable request. Whether fractal-based encryption and non-overlapping segmentation become standard ingredients of future watermarking systems remains to be seen, but the work illustrates a clear trend: the fight to authenticate digital audio is increasingly fought with hybrid tools, marrying signal processing classics like wavelet transforms and windowing functions to deep networks and, now, to the jagged elegance of nowhere-differentiable fractals. For an industry drowning in unattributable sound, every percentage point of extraction accuracy counts.

Subject of Research: A robust deep learning-based audio watermarking system using Blancmange curve cryptography for digital media security

Article Title: A robust audio watermarking system with SC-SGELU-DCNN and BCC algorithm for security

Article References: Patil, A., Shelke, R., & Hiran, D. (2026). A robust audio watermarking system with SC-SGELU-DCNN and BCC algorithm for security. Multimedia Tools and Applications, 85(9), Article 725. https://doi.org/10.1007/s11042-026-21865-8

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21865-8

Keywords: audio watermarking, deep learning, convolutional neural network, Blancmange curve cryptography, discrete wavelet transform, bit error rate, digital security, copyright protection, signal processing, SGELU activation, optimization, multimedia forensics

The core problem the researchers tackle is a familiar one in multimedia security: fragile watermarking. In a fragile watermarking scheme, a small amount of auxiliary data is embedded into the pixels of an image before it is distributed. If someone later modifies the image, the embedded data no longer matches the modified content, and the mismatch reveals the tampering. The difficulty is that conventional schemes often localize tampering only approximately, and they frequently fail to recover the altered content, particularly when the attacker tampers with large regions or when the embedding strategy itself creates ambiguities about which blocks are authentic and which are not.

The new framework begins with tamper detection, and this is where perfect hashing enters the picture. Hash functions compress data into short index values that act like fingerprints; a cryptographic hash is designed so that two different inputs almost never produce the same output. In image authentication, however, the fingerprints must be embedded inside the image itself, which forces them to be very short, and short fingerprints are prone to collisions, situations in which two different image blocks produce identical authentication codes. A collision is dangerous because an attacker can swap the contents of two blocks that share the same code, and the tampering would go undetected. To close this loophole, the team introduces a rehashing-based perfect hashing mechanism built on Schur decomposition. In linear algebra, the Schur decomposition factors a matrix into a unitary transformation and an upper triangular matrix, and the researchers exploit this structure to generate collision-resistant indices for encoding authentication information. Under the random mapping conditions that arise during block-based embedding, the rehashing process ensures that each block receives a unique, verifiable index, dramatically improving the reliability of tamper localization.

Detecting tampering, however, is only half of the task. The second half is recovery, and for that the embedded watermark must carry enough information to reconstruct the original content of any region that comes under attack. Encoding a full image inside itself is impossible, so the team relies on compression. They use the Set Partitioning in Hierarchical Trees algorithm, or SPIHT, a well-established embedded wavelet coding technique that exploits the tree structure of wavelet coefficients to represent image content efficiently. SPIHT produces a compact, progressively refinable bitstream, which means the most visually important information about an image is encoded first and can be represented in very few bits. By embedding SPIHT-encoded recovery data alongside the authentication bits, the scheme ensures that every tampered block carries within its neighbors, or in blocks elsewhere in the image, a compact description of what the original content looked like.

The clever architectural move is that both the authentication data and the recovery data travel together inside a single watermark payload. The two streams are jointly embedded into the host image through an adaptive matrix-guided watermarking strategy. The matrix coding mechanism organizes the image into a structured array of blocks and determines, adaptively, where and how to place the payload so that the embedding capacity is maximized while the visual distortion of the watermarked image remains imperceptible. The word adaptive matters here: rather than applying a uniform embedding rule everywhere, the scheme balances the competing demands of payload size and image quality, deciding how many bits can be hidden in each region without degrading the picture in ways a human viewer or statistical analysis could detect.

When a suspicious image arrives at the verification stage, the process runs in reverse. The receiver recomputes the perfect-hashing indices for each block, compares them with the embedded authentication codes, and flags the blocks where the two disagree as tampered. Because the indices are collision-resistant, the localization is precise, and false alarms caused by index duplication are largely eliminated. The receiver then extracts the SPIHT-encoded recovery bitstream from the intact portions of the image and uses it to reconstruct the content of the flagged regions. The term successive recovery refers to the ability of the scheme to keep refining and restoring tampered areas even when the tampering is extensive, working progressively through the image rather than giving up once a critical fraction of blocks has been corrupted.

Extensive experiments reported in the paper demonstrate that the proposed scheme outperforms representative fragile watermarking-based image authentication methods on both localization accuracy and recovery quality, while maintaining satisfactory perceptual fidelity of the watermarked image. In practice, this means fewer tampered blocks escape detection, fewer authentic blocks are wrongly accused, and the reconstructed regions retain more of their original visual content than with competing approaches. The experiments were conducted on standard grayscale test images drawn from the publicly available USC-SIPI Image Database, providing a common benchmark that allows fair comparison with earlier schemes in the literature.

The significance of this work extends beyond an incremental improvement in watermarking metrics. Images now serve as evidence in courts, as inputs to artificial intelligence systems, as medical records, and as news documents consumed by billions of people. As generative tools make manipulation easier and harder to spot by eye, self-authenticating images that can diagnose and repair their own damage offer a form of defense that does not depend on external databases, trusted third parties, or the availability of the original file. The image carries its own certificate of integrity and its own repair kit, and both travel with it wherever it goes.

The research was carried out by Xuejing Li, Jingmin Pan, Tingting Wang, and Fei Cheng of the Anhui Institute of Information Technology in Wuhu, China, with Fei Cheng also affiliated with Hangzhou Dianzi University and Qimin Zhou of Anhui University contributing to experimental verification. Cheng serves as the corresponding author. The work was supported by the Planned Self-financed Project of Wuhu, the Key Project of Higher Education Research of the Anhui Provincial Department of Education, the Anhui Provincial Department of Education College Talent Project, and the Software and System Engineering Research Center of Smart Car at AIIT. The article was received in May 2026, revised in July, accepted in August, and published on 11 September 2026 in volume 85 of Multimedia Tools and Applications as article number 752. As digital forensics races to keep pace with increasingly sophisticated manipulation tools, schemes like this one, which fuse ideas from data structures, matrix analysis, and wavelet compression into a single self-protecting image format, point toward a future in which the question is no longer simply whether a photograph can be trusted, but whether it can heal itself when that trust is broken.

Subject of Research: Image authentication framework using perfect hashing and matrix coding for tamper detection and successive self-recovery of digital images

Article Title: Successive recovery of tampered regions based on perfect hashing and matrix coding mechanism

Article References: Li, X., Pan, J., Wang, T., Cheng, F., & Zhou, Q. (2026). Successive recovery of tampered regions based on perfect hashing and matrix coding mechanism. Multimedia Tools and Applications, 85(9), Article 752. https://doi.org/10.1007/s11042-026-21881-8

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21881-8

Keywords: image authentication, digital watermarking, tamper detection, image self-recovery, perfect hashing, SPIHT, fragile watermarking, matrix coding, Schur decomposition, multimedia security, image forensics, data hiding

The authors trace the technological lineage of deepfake generation back to its foundations in autoencoders and Generative Adversarial Networks. Autoencoders learn compressed representations of faces and can be repurposed to swap one identity onto another by encoding a source face and decoding it with a target-specific decoder. GANs, introduced by Goodfellow and colleagues in 2014, pit a generator against a discriminator in an adversarial game that progressively sharpens synthetic output. Refinements such as Wasserstein loss stabilized training, while conditional and cycle-consistent variants enabled image-to-image translation without paired data. Style-based generator architectures from NVIDIA pushed facial synthesis to photorealistic quality, and successive improvements to StyleGAN eliminated aliasing artifacts that once betrayed generated images to careful observers.

Beyond static imagery, the survey catalogues the techniques behind face swapping, expression manipulation and full-body reenactment. Early systems such as Face2Face demonstrated real-time expression transfer onto RGB video, while open-source toolkits like DeepFaceLab and Faceswap democratized identity replacement. Subject-agnostic frameworks including FSGAN and FaceShifter improved handling of occlusions and pose variation, and neural-texture approaches rendered convincing reenactments from a single image. First-order motion models animate still portraits by transferring motion fields from a driving video. More recently, neural radiance fields and 3D-aware generative models have introduced view-consistent, geometry-aware head synthesis, allowing manipulations that hold up under changing camera angles, a property that historically defeated many two-dimensional forgery pipelines.

The most consequential shift documented in the survey is the rise of diffusion models. Denoising diffusion probabilistic models, which learn to reverse a gradual noising process, have overtaken GANs in image synthesis quality, and latent diffusion architectures made high-resolution generation computationally practical. Extensions to video, including stable video diffusion, AnimateDiff and space-time diffusion models such as Lumiere, now generate coherent motion from text prompts or reference footage. Autoregressive and masked generative transformers offer an alternative paradigm, predicting image tokens sequentially or in masked patches. The survey notes that these text-to-video systems, described by some researchers as early world simulators, threaten to make synthetic media indistinguishable from camera capture, since they produce fewer of the fixed spectral and structural fingerprints that earlier detectors relied upon.

On the defensive side, the authors chart an equally rapid evolution. First-generation detectors hunted for low-level artifacts: inconsistencies in head pose, absent eye-blinking patterns, anomalies in discrete cosine transform coefficients, and statistical signatures in co-occurrence matrices. Convolutional networks trained on these cues, from compact architectures like MesoNet to capsule-based and multi-task models that simultaneously segment manipulated regions, achieved strong benchmark scores. Biological-signal methods such as FakeCatcher exploited subtle photoplethysmographic traces in skin pixels, while phoneme-viseme mismatch analysis caught forgeries where mouth movements failed to align with speech sounds. Frequency-domain approaches mined spectral clues invisible to the human eye, and identity-aware frameworks like ID-Reveal verified that facial embeddings remained consistent across frames.

Contemporary detection has moved decisively toward deep multimodal learning. Vision Transformers, adapted from large-scale image recognition, capture long-range dependencies that convolutional networks miss, and self-supervised pretraining on unlabeled data improves robustness. Multi-modal multi-scale transformers fuse spatial, temporal and frequency information, while cross-modal graph attention networks are designed to expose subtle audio-visual inconsistencies, such as lip movements that do not correspond to the acoustic signal. Temporal coherence learning examines whether facial dynamics flow naturally across frames, and self-blended image training synthesizes forgery-like blending artifacts without requiring paired real-fake data, improving generalization to unseen manipulation methods. Lightweight architectures, quantization and knowledge distillation are being applied to deploy detectors on edge devices for real-time screening.

The survey devotes substantial attention to the datasets that underpin this research, and to their limitations. Benchmarks such as FaceForensics++, the Deepfake Detection Challenge dataset, Celeb-DF, DeeperForensics-1.0 and WildDeepfake progressively raised the bar with higher compression, greater diversity and in-the-wild footage. Newer resources extend coverage to audio-visual forgeries, including FakeAVCeleb and AV-Deepfake1M, multilingual collections such as an Indian deepfake video dataset, and explainable video datasets designed to make detector decisions interpretable. Yet the authors emphasize that models trained on one corpus routinely lose accuracy when evaluated on another, a generalization gap that constitutes the field’s most persistent weakness. Compression during social-media distribution further erases the fine-grained artifacts detectors depend on, and anti-forensic adversarial training has shown that attackers can deliberately perturb forgeries to defeat classifiers.

These vulnerabilities translate directly into societal risk. The survey frames deepfakes as a severe threat to digital trust, privacy and global security, with implications ranging from non-consensual imagery and financial fraud to political disinformation and erosion of evidence in legal proceedings. The authors argue that no single detection paradigm will suffice; instead they call for hybrid systems combining artifact analysis, temporal modeling, biological signals and cross-modal consistency checks, supported by continual benchmarking on unseen generators. They also highlight open research trajectories including explainable detection, robustness to adversarial evasion, federated approaches that preserve privacy, and neural architecture search for efficient detectors, alongside the need for provenance standards and platform-level deployment rather than laboratory-only evaluation.

For researchers and practitioners, the paper is intended as a comprehensive reference that organizes a sprawling, fast-moving literature into a coherent map. Its taxonomy clarifies how generation paradigms, from autoencoders through GANs to diffusion and autoregressive transformers, impose distinct forensic signatures, and how detection architectures must evolve in response. The authors received no specific funding for the work and declare no competing interests. As generative artificial intelligence continues to mature at a pace that outstrips regulation, the survey’s message is clear: the contest between synthesis and detection is structural, not temporary, and building trustworthy, generalizable detection systems is now a foundational requirement for the integrity of the digital information ecosystem.

Subject of Research: Deepfake generation and detection techniques, trends and challenges in synthetic media forensics

Article Title: A comprehensive survey of deepfake generation and detection: techniques, trends, and challenges

Article References: Tyagi, S., Chauhan, N., Raj Patel, A., & Yadav, D. (2026). A comprehensive survey of deepfake generation and detection: techniques, trends, and challenges. Multimedia Tools and Applications, 85(9), Article 753. https://doi.org/10.1007/s11042-026-21910-6

Image Credits: AI Generated

DOI: 10.1007/s11042-026-21910-6

Keywords: deepfake, generative adversarial networks, diffusion models, face swapping, deepfake detection, multimedia forensics, Vision Transformers, audio-visual inconsistency, synthetic media, digital trust, adversarial evasion, deep learning