Deep Neural Networks (DNNs) are known to be vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to clean examples. With the wide applications of DNNs to Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), the vulnerability of SAR deep recognition models has attracted increasing attention. Existing works show that input transformation can effectively improve the black-box attack performance of adversarial examples, but there is little work in the field of SAR-ATR. In this paper, we propose a novel input transformation attack called Image Mixing and Gradient Smoothing (IMGS), which is dedicated to attacking SAR images. IMGS mixes a small portion of another image into the input samples in amplitude and phase with different rates and uses the Local Mean Square Error (LMSE) filter to smooth the gradient. Extensive experiments conducted on the MSTAR dataset demonstrate that IMGS significantly outperforms other input transformation methods, originally designed for attacking visual images, in both white-box and black-box settings. The code is available at https://github.com/JHL-HUST/IMGS.

Deep neural networks (DNNs) are widely used for neural modulation recognition (NMR) in the electronic field and have been shown to be vulnerable to adversarial examples for NMR. In the physical signal communication scenario, the adversarial signal transmitted by the adversary is affected by the channel, resulting in a random time delay with the original signal and causing decay on the attack performance. To ad-dress this issue, we propose the Position-Invariant adversarial attack Method (PIM) that generates the position-invariant adversarial signal by averaging the adversarial signals generated by shifted input signals to mitigate the channel effect on time delay. Our PIM can be easily integrated with other methods to achieve better results. Extensive experiments demonstrate that the proposed method could outperform all baselines for adversarial attacks on NMR under the time delay setting.

We present an approach to localize generic actions in egocentric videos, called temporal action proposals (TAPs), for accelerating the action recognition step. An egocentric TAP refers to a sequence of frames that may contain a generic action performed by the wearer of a head-mounted camera, e.g., taking a knife, spreading jam, pouring milk, or cutting carrots. Inspired by object proposals, this paper aims at generating a small number of TAPs, thereby replacing the popular sliding window strategy, for localizing all action events in the input video. To this end, we first propose to temporally segment the input video into action atoms, which are the smallest units that may contain an action. We then apply a hierarchical clustering algorithm with several egocentric cues to generate TAPs. Finally, we propose two actionness networks to score the likelihood of each TAP containing an action. The top ranked candidates are returned as output TAPs. Experimental results show that the proposed TAP detection framework performs significantly better than relevant approaches for egocentric action detection.

Egocentric videos, which mainly record the activities carried out by the users of wearable cameras, have drawn much research attention in recent years. Due to its lengthy content, a large number of ego-related applications have been developed to abstract the captured videos. As the users are accustomed to interacting with the target objects using their own hands, while their hands usually appear within their visual fields during the interaction, an egocentric hand detection step is involved in tasks like gesture recognition, action recognition, and social interaction understanding. In this work, we propose a dynamic region-growing approach for hand region detection in egocentric videos, by jointly considering hand-related motion and egocentric cues. We first determine seed regions that most likely belong to the hand, by analyzing the motion patterns across successive frames. The hand regions can then be located by extending from the seed regions, according to the scores computed for the adjacent superpixels. These scores are derived from four egocentric cues: contrast, location, position consistency, and appearance continuity. We discuss how to apply the proposed method in real-life scenarios, where multiple hands irregularly appear and disappear from the videos. Experimental results on public datasets show that the proposed method achieves superior performance compared with the state-of-the-art methods, especially in complicated scenarios.

Object proposal detection is an effective way of accelerating object recognition. Existing proposal methods are mostly based on detecting object boundaries, which may not be effective for cluttered backgrounds. In this paper, we leverage stereopsis as a robust and effective solution for generating object proposals. We first obtain a set of candidate bounding boxes through adaptive transformation, which fits the bounding boxes tightly to object boundaries detected by rough depth and color information. A two-level hierarchy composed of proposal and cluster levels is then constructed to estimate object locations in an efficient and accurate manner. Three stereo-based cues "exactness," "focus," and "distribution" are proposed for objectness estimation. Two-level hierarchical ranking is proposed to accurately obtain ranked object proposals. A stereo data set with 400 labeled stereo image pairs is constructed to evaluate the performance of the proposed method in both indoor and outdoor scenes. Extensive experimental evaluations show that the proposed stereo-based approach achieves a better performance than the state of the arts with either a small or a large number of object proposals. As stereopsis can be a complement to the color information, the proposed method can be integrated with existing proposal methods to obtain superior results.

Wearable cameras used to record daily life are attracting researchers' attention, and a large number of ego-related applications have been developed in recent years. Hand detection is one of the key steps for the tasks like gesture recognition, action recognition and understanding hand-based interaction in egocentric videos, since humans are accustomed to interacting with objects using their hands. In this work a novel region growth approach is proposed for egocentric hand detection. The proposed method first identifies seed regions most likely containing the parts of hand based on the distribution of hand-related matches. Then hand regions are gradually located by extending from the seed regions. Finally a whole hand is obtained according to the scores of adjacent superpixels, which are evaluated based on four egocentric cues: contrast, distance, previous hand position and skin appearance. The experimental results on two publicly available datasets demonstrate this work achieves satisfactory performance.

The research from cognitive psychology and neurobiology suggests that people have a strong ability to perceive objects before identifying them, and the human vision system (HVS) processes some salient regions of an image while leaving others nearly unprocessed. Based on this observation, we assume people prefer to pay more attention to salient regions when judging whether images are matched. A novel saliency detection based on reconstruction residual is proposed to calculate saliency maps and generate salient regions, which helps decrease the interference from irrelevant regions in image retrieval. The introduction of integrated region matching (IRM) can better characterize the spatial constraints among salient regions to conduct similarity measurement. The experimental results demonstrate that the proposed saliency detection has a good performance. At the same time, the combination of salient regions and IRM can help improve the state-of-the-art retrieval algorithms.

The need for fast retrieving images has recently increased tremendously in many application areas (biomedicine, military, commerce, education, etc.). In this work, we exploit the saliency detection to select a group of salient regions and utilize an undirected graph to model the dependency among these salient regions, so that the similarity of images can be measured by calculating the similarity of the corresponding graphs. Identification of salient pixels can decrease interferences from irrelevant information, and make the image representation more effective. The introduction of the graph model can better characterize the spatial constraints among salient regions. The comparison experiments are carried out on the three representative datasets publicly available (Holidays, UKB, and Oxford 5k), and the experimental results show that the integration of the proposed method and the SIFT-like local descriptors can better improve the existing state-of-the-art retrieval accuracy.

The need for fast retrieving images has recently increased tremendously in many application areas. SIFT-like local descriptor-based matching is widely adopted and has achieved state-of-the-art performance. However, it becomes inefficient when computational and storage resources are limited. Besides, local descriptor-based methods may suffer difficulties when an image pair contains multiple similar local regions. In this work, we propose a novel and effective filtration strategy based on the Conditional Random Field (CRF) model to enhance image retrieval. As the CRF model is used to depict the dependencies of adjacent components, we regard the essential components of an image as the basic structure of CRF. The novel Fourier Descriptor CRF (FD-CRF) method is first proposed to utilize the advantages of CRF and global shape features, then the filtration strategy is adopted to integrate FD-CRF and SIFT-like descriptors for better retrieval results. The experiments demonstrate that our method is practical and outperforms state-of-the-art methods in matching accuracy.