Modern intensive duck farming has improved production efficiency while facing health problems for ducks. As duck behaviors are closely related to their health status, accurately monitoring their behaviors is necessary. With the development of artificial intelligence (AI), the application of AI offers an effective approach to animal behavior recognition. Currently, accurately recognizing duck behaviors consistently from day to night remains a challenge. This challenge stems from the persistent dynamic changes in light, which can lead to significant performance degradation in conventional behavior recognition methods. To overcome this challenge, this study proposes an enhanced You Only Look Once version 11 small (YOLOv11s) with frequency feature enhancement and illumination perception for duck behavior recognition in dynamic light scenarios (FIY4DBR). Specifically, to tackle the problem of unclear edge, texture, and behavior characteristics of ducks in the low-light condition, a frequency feature enhancement mechanism (FFEM) is designed, which effectively enhances the duck feature representation ability. Additionally, to improve the model’s robustness to light variations, an illumination perception mechanism (IPM) is developed, which adjusts the contrast of objects and background features according to different brightness conditions, thereby enhancing the model’s generalization capability across different brightness scenarios. Experimental simulations on the self-built dataset show that FIY4DBR achieves an average recognition precision of 92.0% and recall of 88.8%, representing improvements of 1.8 and 3.6 percentage points over the baseline YOLOv11s. This demonstrates that the proposed FIY4DBR provides a high-precision and highly adaptive solution for intelligent livestock behavior monitoring, contributing to advancing the development of intelligent farming technologies.

Robust reversible watermarking (RRW) techniques have been proposed in the literature to protect the copyrights of high-fidelity digital images while achieving robustness, reversibility, invisibility, and large capacity simultaneously. Most studies on RRW have been designed to resist common signal processing (CSP) attacks, but only a few can withstand both CSP and geometric deformation (GD) attacks. To address this problem, this study proposes a novel RRW method using a fractional-order polar complex exponential transform (FrPCET) and optimized quantization index modulation (QIM). Specifically, the optimal fractional parameter of the FrPCET is determined through numerical simulation experiments using the criterion of minimum image reconstruction errors. The stability of FrPCET moments against CSP is evaluated by performing attack simulation tests on 500 images, revealing that differences between specific pairs of FrPCET moments exhibits similar variation patterns under attacks, thus making them suitable for use as embedding carriers. Then, the watermark is embedded by optimizing a conventional QIM, which improves the robustness of the watermark under the same image quality conditions. The distortions caused by watermark embedding and the hash sequences used for integrity authentication are subsequently taken as the auxiliary information and are reversibly embedded via the prediction error expansion-histogram shift method. After receiving the watermarked image, the receiver performs an inverse operation to recover both the watermark and the original image in the absence of attacks; otherwise, it only extracts the watermark. Extensive simulation experiments demonstrate that the proposed method has greater robustness against various CSP and GD attacks than do state-of-the-art methods under the same embedding capacity and invisibility. This indicates the feasibility and effectiveness of the proposed scheme.

For copyright protection and perfect recovery of the original image in case of no attacks, it is necessary to develop robust reversible watermarking (RRW) methods that counteract both common signal processing (CSP) and geometric deformation (GD) attacks (RRW-CG). However, to the best of our knowledge, none of the existing RRW methods exploit target attacks as prior knowledge to improve their robustness and embedding capacity. To this end, we propose a two-stage RRW-CG scheme with attack-simulation-based adaptive normalization and embedding. Specifically, the polar harmonic transform (PHT) moments are taken as watermark carriers, and their stability with respect to target attacks is evaluated by performing attack simulation tests on large-scale images. This enables the adaptive normalization of PHT moments to improve the watermark robustness. The PHT moments with high stability are then chosen as watermark carriers, and the conventional spread transform dither modulation (STDM) with one quantization level is optimized to form the enhanced version with multiple quantization levels, in which the embedding strength is determined adaptively via attack simulation tests on the candidate watermarked image. This in turn improves the watermark robustness and increases the embedding capacity. After the robust watermark has been embedded, errors caused by robust watermarking are used as the auxiliary information and then inserted into the robustly watermarked image via the recursive code-based reversible watermarking technique, ensuring the reversibility in case of no attacks. Extensive experimental simulation results show that the proposed scheme outperforms the state-of-the-art RRW methods in terms of robustness against CSP such as AWGN, JPEG, JPEG2000, mean filtering, and median filtering as well as GD including rotation and scaling under the same invisibility, reversibility, and embedding capacity. This indicates that, by exploiting target attacks as prior knowledge and designing the attack-simulation-based adaptive normalization and embedding, the proposed novel RRW is feasible and effective.

The issue of aviation safety has always received a great of attention and focus, and birds are also an important issue in aviation safety. Nowadays, drones have emerged and share the same airspace with birds at low altitudes. The problems associated with drones should also be taken into account. For example, small drones can be misused for illegal activities and the threat from them is on the rise. Driven by this situation, we used data provided by the ICASSP Drone-vs-Bird detection Grand Challenge for drone detection and used the method of adding shallow feature pyramid network and attention model on SSD [1] (SFA-SSD) to solve the problem of drone detection in competition. Out of 30 test videos, our method was able to detect drones in 11 videos, with 8 videos scoring above 0.1 and only 3 videos scoring above 0.7.

The robust reversible watermarking (RRW) requires high robustness and capacity on the condition of reversibility and imperceptibility, which still remains a big challenge nowadays. In this paper, we propose a two-stage RRW scheme that improves robustness and capacity through embedding optimization and rounded error compensation. The first stage inserts a robust watermark into the selected Pseudo-Zernike moments (PZMs) by using an adaptive normalization method and an optimized embedding strategy. Specifically, the adaptive normalization method achieves both an invariance to pixel amplitude variation and a balance between robustness and imperceptibility, and the optimized embedding strategy reduces embedding distortions remarkably. The watermarked PZMs are inversely transformed to generate the robustly watermarked image, in which rounded errors caused in the inverse transformation is compensated elaborately and thus a larger capacity can be obtained at the same embedding distortion. The second stage embeds a reversible watermark consisting of errors between the robust watermark embedded image and the original one, aiming at achieving the reversibility in case of no attacks. Extensive experimental simulations show that the proposed scheme provides strong robustness against common signal processing, including AWGN, salt-and-pepper noise, JPEG, JPEG2000, median filtering, mean filtering, geometrical transformations involving rotation and scaling, and a compressive sensing attack exemplified by two-dimensional compressive sensing, which outperforms the state-of-the-art schemes. Our code is available athttps://github.com/yichao-tang/PZMs-RRW.

As encryption masks the content of the original image and thus statistical characteristics of the original image cannot be used to compress the encrypted version, compressing an encrypted image efficiently remains a significant challenge today. In this study, a novel encryption-then-lossy-compression (ETLC) scheme was developed using nonuniform downsampling and a customized deep network. Specifically, the nonuniform downsampling method integrates both uniform and random sampling to achieve an arbitrary compression ratio for an encrypted image. Lossy reconstruction from the decrypted and decompressed image is described as a constrained optimization problem, and an ETLC-oriented customized deep neural network (ETCNN) is elaborately designed to solve this problem. ETCNN contains three parts: channel-wise non-local attention including residual group and non-local sparse attention, a residual content supplementation (RCS), and a downsampling constraint (DC), where RCS and DC are customized modules exploiting specific features of the downsampling-based ETLC system. Extensive experimental simulations show that the proposed scheme outperforms the state-of-the-art ETLC methods remarkably, indicating the feasibility and effectiveness of the proposed scheme exploiting the nonuniform downsampling and ETCNN-based reconstruction. Code is available athttps://github.com/hujuanzp/ETCNN.

Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.

Similar to conventional compression with the original, unencrypted image as the input, the recently emerged compression on encrypted images generally exploits statistical correlation of natural images to improve compression efficiency. Most of these compression schemes in the literature leverage statistical correlation at the content-owner or service-provider side, which would either increase the computational burden on the content-owner or disclose statistical distributions to the service-provider and thus probably hinder their practical applications. Through analysis on properties of the compression system for encrypted data, we believe that it is more preferable to exploit statistical correlation of natural images at the receiver side with both encryption key and sufficient computational capability, which in turn would improve compression efficiency while achieving low computational complexity and sufficient security for the content owner and the service provider. In light of this, we use the Markov random field (MRF) to characterize binary images in the spatial domain and represent it with a factor graph. The constructed MRF representation of the binary image in the factor graph is then integrated seamlessly with the factor graph for low-density parity check (LDPC)-based decompression, yielding a joint factor graph for binary image reconstruction. By deriving message update equations for the joint factor graph, we develop a new lossless compression scheme for encrypted binary images, which involves stream-cipher-based encryption, LDPC-based compression, and factor-graph-based image reconstruction. Preferable parameters for the proposed scheme are first determined numerically on a specific binary image and then applied to other binary images. Extensive simulations show that significant improvements in terms of compression bit rate over the state of the art are achieved, demonstrating the feasibility and effectiveness of the proposed scheme.

Image retargeting is a critical technique in displaying images on devices with different resolutions. This study presents a new image retargeting algorithm based on aesthetic‐based cropping and scaling. A composite measurement is first constructed under the guidelines of composition aesthetics in photographing. An aesthetic‐based cropping is proposed to yield an optimal candidate retargeted image with maximum aesthetic value computed via a constructed composite measurement. The optimal candidate is uniformly scaled to obtain the retargeted image of target size. Some subjective and objective assessments demonstrate that the proposed scheme significantly improves the aesthetics of retargeted images while preserving the important objects. It also achieves better performance in terms of aesthetics than a number of conventional image retargeting approaches.

Achieving robustness, imperceptibility and high capacity simultaneously is of great importance in digital watermarking. This paper presents a new informed image watermarking scheme with high robustness and simplified complexity at an information rate of 1/64 bit/pixel. Firstly, a Taylor series approximated locally optimum test (TLOT) detector based on the hidden Markov model (HMM) in the wavelet domain is developed to tackle the problem of unavailability of exact embedding strength in the receiver due to informed embedding. Then based on the TLOT detector and the concept of dirty-paper code design, new HMM-based spherical codes are constructed to provide an effective tradeoff between robustness and distortion. The process of informed embedding is formulated as an optimization problem under the robustness and distortion constraints and the genetic algorithm (GA) is then employed to solve this problem. Moreover, the perceptual distance in the wavelet domain is also developed and incorporated into the GA-based optimization. Simulation results demonstrate that the proposed informed watermarking algorithm has high robustness against common attacks in signal processing and shows a comparable performance to the state-of-the-art scheme with a greatly reduced arithmetic complexity.

The robust performance against geometrical manipulations is still one of major concerns in robust watermarking although significant improvement has been achieved in past decades. In this paper, we tackle the global geometrical attacks by designing a deformable multi-scale transform (DMST) that has joint shiftability in position, orientation, and scale. Via DMST, we both derive theoretically the principles for geometrical synchronization and develop a template-based scheme to efficiently estimate geometrical parameters. Also, the hidden Markov model in the standard wavelet domain is extended to the steerable wavelet domain and further used to improve the performance of watermark extraction. Experimental simulation demonstrates that the proposed watermarking scheme is quite robust to the common signal processing, geometrical attacks, and their joint attacks.

A new informed image watermarking algorithm is presented in this paper, which can achieve the information rate of 1/64 bits/pixel with high robustness. Firstly, a LOT (local optimal test) detector based on HMM in wavelet domain is developed to tackle the issue that the exact strength for informed embedding is unknown to the receiver. Then based on the LOT detector, the dirty-paper code for informed coding is constructed and the metric for the robustness is defined accordingly. Unlike the previous approaches of informed watermarking which take the informed coding and embedding process separately, the proposed algorithm implements a joint coding and embedding optimization for high capacity and robust watermarking. The genetic algorithm (GA) is employed to optimize the robustness and distortion constraints simultaneously. Experimental results show that the proposed algorithm achieves significant improvements in performance against JPEG, gain attack, low-pass filtering and etc.

A DWT-HMM (hidden Markov model in wavelet domain) image watermarking algorithm with content-adaptive approach is proposed in this paper to optimized the trade-off between robustness and visual quality, which is characterized as follows: the entropy mask proposed by Watson is constructed in wavelet domain; the entropy mask and the new developed integrated HVS are used as the measures to adaptively select image components for watermarking; repeat-accumulation (RA) code with erasure and error correction is employed to synchronize the watermarked image; and a posterior HMM is utilized in watermark detection. Considerable improvement in robustness performance with the proposed adaptive algorithm is obtained over the previous DWT-HMM watermarking algorithm with stochastic embedding.