Visual object tracking methods utilizing onboard cameras have significantly advanced the widespread application of unmanned aerial vehicles (UAVs). However, the stochastic and intricate noise inherent in camera systems has critically impeded the performance of UAV trackers, particularly under low-light conditions. To solve this problem, this letter presents an efficient wavelet-based denoising transformer (WTM) integrated with a fast Fourier adjustment module (FFAM) to reduce random real noise, thereby improving UAV nighttime tracking performance. Specifically, an encoder-latent-decoder structure is designed for efficient end-to-end transformation. Additionally, the WTM in both the encoder and the decoder block introduces channel-wise transformer to extract low-frequency information. The FFAM is utilized in the latent block to adjust local texture details. Finally, a novel residual feedforward network is designed to enhance the processing of high-frequency information. Extensive experimental results validate the effectiveness of our proposed method, demonstrating significant improvements in UAV nighttime tracking capabilities by adapting to diverse enhancers and tracking algorithms.

Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. They also prevent people from misusing images, especially those generated by AI models. We propose a family of regeneration attacks to remove these invisible watermarks. The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image. This approach is flexible and can be instantiated with many existing image-denoising algorithms and pre-trained generative models such as diffusion models. Through formal proofs and extensive empirical evaluations, we demonstrate that pixel-level invisible watermarks are vulnerable to this regeneration attack. Our results reveal that, across four different pixel-level watermarking schemes, the proposed method consistently achieves superior performance compared to existing attack techniques, with lower detection rates and higher image quality. However, watermarks that keep the image semantically similar can be an alternative defense against our attacks. Our finding underscores the need for a shift in research/industry emphasis from invisible watermarks to semantic-preserving watermarks. Code is available at https://github.com/XuandongZhao/WatermarkAttacker

In this article, we present a smooth low-rank tensor completion (SLRTC) based reconstruction algorithm to recover raw thermal image sequences from highly randomly undersampled or small numbers of available thermographic data. The presented algorithm is also fused with a temporal interpolation algorithm (to produce the SLRTCTI algorithm) to complement high frame-rate thermal image sequences with notably enhanced thermal contrast. Pulsed and lock-in thermographic data are obtained for subsurface defects in carbon fiber reinforced polymer (CFRP) to demonstrate the performance of the algorithm, and it is shown that the algorithm is data-driven and is independent of the excitation form. The algorithm enables the maximum available frame rates of thermal infrared cameras to be increased by at least ten times. To further enhance the visibility of the CFRP defects in the results reconstructed using the SLRTC algorithm, fast randomized sparse principal component thermography (FRSPCT) and 2-D principal component thermography (TDPCT) are also proposed. Results show that TDPCT remarkably enhances the thermal contrast between the defective and intact regions under highly undersampled data conditions. In addition, FRSPCT provides more easily interpretable detection results and highlights the hidden details of irregularly-shaped abnormal defects.

Due to the decentralization, irreversibility, and traceability, blockchain has attracted significant attention and has been deployed in many critical industries such as banking and logistics. However, the micro-architecture characteristics of blockchain programs still remain unclear. What's worse, the large number of micro-architecture events make understanding the characteristics extremely difficult. We even lack a systematic approach to identify the important events to focus on. In this paper, we propose a novel benchmarking methodology dubbed BBS to characterize blockchain programs at micro-architecture level. The key is to leverage fuzzy set theory to identify important micro-architecture events after the significance of them is quantified by a machine learning based approach. The important events for single programs are employed to characterize the programs while the common important events for multiple programs form an importance vector which is used to measure the similarity between benchmarks. We leverage BBS to characterize seven and six benchmarks from Blockbench and Caliper, respectively. The results show that BBS can reveal interesting findings. Moreover, by leveraging the importance characterization results, we improve that the transaction throughput of Smallbank from Fabric by 70% while reduce the transaction latency by 55%. In addition, we find that three of seven and two of six benchmarks from Blockbench and Caliper are redundant, respectively.