In recent years, unlearnable data have emerged as an effective way to prevent unauthorized exploitation of personal data by intentionally preventing model learning meaningful patterns during training, while ensuring that the processed data remain visually indistinguishable from the original. Previous approaches, such as error-minimizing noise methods, have yielded promising results in disrupting model learning but often lack robustness against adversarial training. More recently, convolution-based techniques have been introduced to address this limitation, enhancing resistance to adversarial training, but at the cost of sacrificing high-frequency image details. In this study, we propose a novel method called Wavelet Domain Steganography (WDS) that steganographically writes class-specific information in the wavelet transform domain. By leveraging the inherent multi-resolution and localized properties of wavelets, WDS allows for precise control over the steganographic information injected into different frequency subbands. The proposed method preserves high-frequency details and maintains visual fidelity while effectively inhibiting the learning of semantic information by deep neural networks. Experimental evaluations show that WDS exhibits robust unlearnability under common data augmentations and adversarial training scenarios, thereby achieving an improved balance between imperceptibility and unlearnability relative to prior methods. • A new paradigm of wavelet-domain steganography is proposed for robust unlearnable data generation. • Frequency-aware perturbations achieve superior robustness–imperceptibility trade-offs without surrogate optimization. • DNN frequency bias is systematically characterized and leveraged to impair generalization.

Real-world multivariate time series unsupervised anomaly detection is a challenging problem due to intricate temporal correlations. Recently, impressive progress have been made in tackling this issue through the design of large-scale models, facilitated by the growing model parameters. However, in resource-constrained scenarios such as ubiquitous computing and edge computing, the large-scale models suffer from issues like high parameter complexity and expensive training overheads. Existing methods can only strive for a direct tradeoff between model size and performance. To address this challenge, we propose DiMER (Diminutive Memory-Enhanced Reconstruction), a model with parameters of the order of 0.1M. In DiMER, we introduce a novel contrast memory mechanism to learn normal patterns with diminutive network and propose a temporal reconstruction loss nto add the autocorrelation information. In addition, we introduce a multi-space composite detection criterion, an anomaly score calculation that takes into account both memory space and data space. Extensive experiments on real-world datasets across various domains demonstrate that the proposed model achieves comparable or even superior performance to large-scale models while maintaining lightweight.

Detecting anomalies in unsupervised multivariate time series is challenging due to the intricate temporal patterns present in both local short-term and global long-term dependencies. Long short-term memory has achieved impressive results in this domain, yet it is gradually being supplemented by Transformers, due to limitations such as non-parallelization, gradient vanishing, and difficulty in focusing on local information. Leveraging the attention mechanism, Transformers can attend to all time steps simultaneously, effectively capturing local dependencies. However, they may often face challenges in efficiently modeling long-term dependencies, particularly in real-world scenarios. To address these issues, we propose the HYbrid Memory and Attention Network (HYMAN), which integrates memory and attention mechanisms together to model both global and local information. The attention captures short-term dependencies by focusing on temporal autocorrelation, while the memory stores and updates key historical patterns in global information, facilitating the learning of long-term dependencies. In contrast to previous approaches, HYMAN eliminates the need for auxiliary loss, simplifying the training by reducing the effort for coefficients tuning. In the inference phase, HYMAN introduces a novel anomaly scoring method that fuses features from both the temporal and latent spaces, offering high-performance detection compared to traditional methods that rely solely on reconstruction. Extensive experiments on real-world benchmark datasets demonstrate that HYMAN achieves state-of-the-art performance by leveraging the complementary strengths of memory and attention mechanisms.

Unsupervised anomaly detection is to detect previously unseen rare samples without any prior knowledge about them. With the emergence of deep learning, many methods employ normal data reconstruction to train detection models, which is expected to yield relatively large errors when reconstructing anomalies. However, recent studies find that anomalies can be overgeneralized, resulting in reconstruction errors as small as normal samples. In this paper, we examine the anomaly overgeneralization problem and propose global semantic information learning. Normal and anomalous samples may share the same local feature such as textures, edges, and corners, but have separability at the global semantic level. To address this, we propose a novel cascade memory architecture designed to capture global semantic information in the latent space and introduce a configurable sparsification and random forgetting mechanism. Our proposed method achieves state-of-the-art experimental results on different public benchmarks, without the introduction of any additional auxiliary loss terms. The code is available at https://github.com/LiJiahao-Alex/Cascade-Memory.

Unsupervised anomaly detection is a binary classification that detects anomalies in unseen samples given only unlabeled normal data. Reconstruction-based approaches are widely used, which perform reconstruction error minimization on training data to learn normal patterns and quantify the degree of anomalies by reconstruction errors on testing data. However, this approach tends to miss anomalies when the normal data has multi-pattern. Because the model generalizes unrestrictedly beyond normal patterns even to include anomaly patterns. In this paper, we proposed a memory mechanism that memorizes typical normal patterns through a capacity-controlled external differentiable matrix so that the generalization of the model to anomalies is limited by the retrieval of the matrix. We achieved state-of-the-art performance on several public benchmarks.

Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders affecting children. Because the etiology of ADHD is complex and its symptoms are not specific, there is a lack of feasible quantitative diagnostic methods. Pursuing objective and non-invasive detection methods and standards is of great practical significance to prevent the development of the disease. In this study, we aim to address one specific concern about the objectivity and quantification of ADHD diagnosis. Over a year, we iteratively designed and tested WeDA, a scale-driven wearable diagnostic assessment system. This system contains an Android computer machine with a large touchscreen, a suite of 3D printed interactive devices, and six wearable motion sensors. We implement ten diagnostic tasks drawing on the symptoms of ADHD based on DSM-5. The experimental results of classifying children with ADHD and typically developing children and subjective evaluations from doctors, parents, and children validate the effectiveness and acceptability of WeDA.

Sign Language Synthesis (SLS) is a domain-specific problem where multiple sign language words are stitched to generate a whole sentence in video, which serves to facilitate communications between the hearing-impaired people and healthy population. This paper presents a Variable Motion Frame Interpolation (VMFI) method for highly fluent SLS in scattered videos. Existing approaches for SLS mainly focus on mechanical virtual human technology, lacking high flexibility and natural effect. Also, the representative solutions to interpolate frames usually assume that the motion object moves at a constant speed which is not suitable for predicting the complex hand motion in frames of scattered sign language videos. To address the above issues, the proposed VMFI adopts acceleration to predict more accurate interpolated frames based on an end-to-end convolutional neural network. The framework of VMFI consists of variable optical flow estimation network and high-quality frame synthesis network that can approximate and fuse the intermediate optical flow to generate interpolated frames for synthesis. Experimental results on our realistic collected Chinese sign language dataset demonstrate that the proposed VMFI model achieves efficiency by performing better in PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity) and MA (Motion Activity) and gets higher score in MOS (Mean Opinion Score) than other two representative methods.