Robust Image Watermarking based on Cross-Attention and Invariant Domain Learning

Image watermarking involves embedding and extracting watermarks within a cover image, with deep learning approaches emerging to bolster generalization and robustness. Predominantly, current methods employ convolution and concatenation for watermark embedding, while also integrating conceivable augmentation in the training process. This paper explores a robust image watermarking methodology by harnessing cross-attention and invariant domain learning, marking two novel, significant advancements. First, we design a watermark embedding technique utilizing a multi-head cross attention mechanism, enabling information exchange between the cover image and watermark to identify semantically suitable embedding locations. Second, we advocate for learning an invariant domain representation that encapsulates both semantic and noise-invariant information concerning the watermark, shedding light on promising avenues for enhancing image watermarking techniques.

ViewMix: Augmentation for Robust Representation in Self-Supervised Learning

Joint Embedding Architecture-based self-supervised learning methods have attributed the composition of data augmentations as a crucial factor for their strong representation learning capabilities. While regional dropout strategies have proven to guide models to focus on lesser indicative parts of the objects in supervised methods, it hasn't been adopted by self-supervised methods for generating positive pairs. This is because the regional dropout methods are not suitable for the input sampling process of the self-supervised methodology. Whereas dropping informative pixels from the positive pairs can result in inefficient training, replacing patches of a specific object with a different one can steer the model from maximizing the agreement between different positive pairs. Moreover, joint embedding representation learning methods have not made robustness their primary training outcome. To this end, we propose the ViewMix augmentation policy, specially designed for self-supervised learning, upon generating different views of the same image, patches are cut and pasted from one view to another. By leveraging the different views created by this augmentation strategy, multiple joint embedding-based self-supervised methodologies obtained better localization capability and consistently outperformed their corresponding baseline methods. It is also demonstrated that incorporating ViewMix augmentation policy promotes robustness of the representations in the state-of-the-art methods. Furthermore, our experimentation and analysis of compute times suggest that ViewMix augmentation doesn't introduce any additional overhead compared to other counterparts.

Imperceptible Adversarial Attack on Deep Neural Networks from Image Boundary

Although Deep Neural Networks (DNNs), such as the convolutional neural networks (CNN) and Vision Transformers (ViTs), have been successfully applied in the field of computer vision, they are demonstrated to be vulnerable to well-sought Adversarial Examples (AEs) that can easily fool the DNNs. The research in AEs has been active, and many adversarial attacks and explanations have been proposed since they were discovered in 2014. The mystery of the AE's existence is still an open question, and many studies suggest that DNN training algorithms have blind spots. The salient objects usually do not overlap with boundaries; hence, the boundaries are not the DNN model's attention. Nevertheless, recent studies show that the boundaries can dominate the behavior of the DNN models. Hence, this study aims to look at the AEs from a different perspective and proposes an imperceptible adversarial attack that systemically attacks the input image boundary for finding the AEs. The experimental results have shown that the proposed boundary attacking method effectively attacks six CNN models and the ViT using only 32% of the input image content (from the boundaries) with an average success rate (SR) of 95.2% and an average peak signal-to-noise ratio of 41.37 dB. Correlation analyses are conducted, including the relation between the adversarial boundary's width and the SR and how the adversarial boundary changes the DNN model's attention. This paper's discoveries can potentially advance the understanding of AEs and provide a different perspective on how AEs can be constructed.

Deep Learning based Image Watermarking: A Brief Survey

The act of secretly embedding and extracting a watermark on a cover image to protect it is known as image watermarking. In recent years, deep learning-based image watermarking techniques have been emerging one after another. To study the state-of-the-art, this survey categorizes cutting-edge deep learning-based image watermarking techniques into Embedder-Extractor Joint Training, Deep Networks as a Feature Transformation, and Hybrid schemes. Research directions in each category are also analyzed and summarized. Additionally, potential future research directions are discussed to envision future studies.

DeepTextMark: Deep Learning based Text Watermarking for Detection of Large Language Model Generated Text

The capabilities of text generators have grown with the rapid development of Large Language Models (LLM). To prevent potential misuse, the ability to detect whether texts are produced by LLM has become increasingly important. Several related works have attempted to solve this problem using binary classifiers that categorize input text as human-written or LLM-generated. However, these classifiers have been shown to be unreliable. As impactful decisions could be made based on the result of the classification, the text source detection needs to be high-quality. To this end, this paper presents DeepTextMark, a deep learning-based text watermarking method for text source detection. Applying Word2Vec and Sentence Encoding for watermark insertion and a transformer-based classifier for watermark detection, DeepTextMark achieves blindness, robustness, imperceptibility, and reliability simultaneously. As discussed further in the paper, these traits are indispensable for generic text source detection, and the application focus of this paper is on the text generated by LLM. DeepTextMark can be implemented as an "add-on" to existing text generation systems. That is, the method does not require access or modification to the text generation technique. Experiments have shown high imperceptibility, high detection accuracy, enhanced robustness, reliability, and fast running speed of DeepTextMark.

Padding Module: Learning the Padding in Deep Neural Networks

During the last decades, many studies have been dedicated to improving the performance of neural networks, for example, the network architectures, initialization, and activation. However, investigating the importance and effects of learnable padding methods in deep learning remains relatively open. To mitigate the gap, this paper proposes a novel trainable Padding Module that can be placed in a deep learning model. The Padding Module can optimize itself without requiring or influencing the model's entire loss function. To train itself, the Padding Module constructs a ground truth and a predictor from the inputs by leveraging the underlying structure in the input data for supervision. As a result, the Padding Module can learn automatically to pad pixels to the border of its input images or feature maps. The padding contents are realistic extensions to its input data and simultaneously facilitate the deep learning model's downstream task. Experiments have shown that the proposed Padding Module outperforms the state-of-the-art competitors and the baseline methods. For example, the Padding Module has 1.23% and 0.44% more classification accuracy than the zero padding when tested on the VGG16 and ResNet50.

Foreign Object Debris Detection for Airport Pavement Images based on Self-supervised Localization and Vision Transformer

Supervised object detection methods provide subpar performance when applied to Foreign Object Debris (FOD) detection because FOD could be arbitrary objects according to the Federal Aviation Administration (FAA) specification. Current supervised object detection algorithms require datasets that contain annotated examples of every to-be-detected object. While a large and expensive dataset could be developed to include common FOD examples, it is infeasible to collect all possible FOD examples in the dataset representation because of the open-ended nature of FOD. Limitations of the dataset could cause FOD detection systems driven by those supervised algorithms to miss certain FOD, which can become dangerous to airport operations. To this end, this paper presents a self-supervised FOD localization by learning to predict the runway images, which avoids the enumeration of FOD annotation examples. The localization method utilizes the Vision Transformer (ViT) to improve localization performance. The experiments show that the method successfully detects arbitrary FOD in real-world runway situations. The paper also provides an extension to the localization result to perform classification; a feature that can be useful to downstream tasks. To train the localization, this paper also presents a simple and realistic dataset creation framework that only collects clean runway images. The training and testing data for this method are collected at a local airport using unmanned aircraft systems (UAS). Additionally, the developed dataset is provided for public use and further studies.

An Improved Normed-Deformable Convolution for Crowd Counting

In recent years, crowd counting has become an important issue in computer vision. In most methods, the density maps are generated by convolving with a Gaussian kernel from the ground-truth dot maps which are marked around the center of human heads. Due to the fixed geometric structures in CNNs and indistinct head-scale information, the head features are obtained incompletely. Deformable convolution is proposed to exploit the scale-adaptive capabilities for CNN features in the heads. By learning the coordinate offsets of the sampling points, it is tractable to improve the ability to adjust the receptive field. However, the heads are not uniformly covered by the sampling points in the deformable convolution, resulting in loss of head information. To handle the non-uniformed sampling, an improved Normed-Deformable Convolution (\textit{i.e.,}NDConv) implemented by Normed-Deformable loss (\textit{i.e.,}NDloss) is proposed in this paper. The offsets of the sampling points which are constrained by NDloss tend to be more even. Then, the features in the heads are obtained more completely, leading to better performance. Especially, the proposed NDConv is a light-weight module which shares similar computation burden with Deformable Convolution. In the extensive experiments, our method outperforms state-of-the-art methods on ShanghaiTech A, ShanghaiTech B, UCF\_QNRF, and UCF\_CC\_50 dataset, achieving 61.4, 7.8, 91.2, and 167.2 MAE, respectively. The code is available at https://github.com/bingshuangzhuzi/NDConv

Perspective Transformation Layer

Incorporating geometric transformations that reflect the relative position changes between an observer and an object into computer vision and deep learning models has attracted much attention in recent years. However, the existing proposals mainly focus on affine transformations that cannot fully show viewpoint changes. Furthermore, current solutions often apply a neural network module to learn a single transformation matrix, which ignores the possibility for various viewpoints and creates extra to-be-trained module parameters. In this paper, a layer (PT layer) is proposed to learn the perspective transformations that not only model the geometries in affine transformation but also reflect the viewpoint changes. In addition, being able to be directly trained with gradient descent like traditional layers such as convolutional layers, a single proposed PT layer can learn an adjustable number of multiple viewpoints without training extra module parameters. The experiments and evaluations confirm the superiority of the proposed PT layer.

DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Blind Detection

Generalized optical multiple-input multiple-output (GOMIMO) techniques have been recently shown to be promising for high-speed optical wireless communication (OWC) systems. In this paper, we propose a novel deep learning-aided GOMIMO (DeepGOMIMO) framework for GOMIMO systems, where channel state information (CSI)-free blind detection can be enabled by employing a specially designed deep neural network (DNN)-based MIMO detector. The CSI-free blind DNN detector mainly consists of two modules: one is the pre-processing module which is designed to address both the path loss and channel crosstalk issues caused by MIMO transmission, and the other is the feed-forward DNN module which is used for joint detection of spatial and constellation information by learning the statistics of both the input signal and the additive noise. Our simulation results clearly verify that, in a typical indoor 4 $\times$ 4 MIMO-OWC system using both generalized optical spatial modulation (GOSM) and generalized optical spatial multiplexing (GOSMP) with unipolar non-zero 4-ary pulse amplitude modulation (4-PAM) modulation, the proposed CSI-free blind DNN detector achieves near the same bit error rate (BER) performance as the optimal joint maximum-likelihood (ML) detector, but with much reduced computational complexity. Moreover, since the CSI-free blind DNN detector does not require instantaneous channel estimation to obtain accurate CSI, it enjoys the unique advantages of improved achievable data rate and reduced communication time delay in comparison to the CSI-based zero-forcing DNN (ZF-DNN) detector.