With the rise of artificial intelligence, deep learning is gradually applied to the field of agriculture and plant science. However, the excellent performance of deep learning needs to be established on massive numbers of samples. In the field of plant science and biology, it is not easy to obtain a large amount of labeled data. The emergence of few-shot learning solves this problem. It imitates the ability of humans' rapid learning and can learn a new task with only a small number of labeled samples, which greatly reduces the time cost and financial resources. At present, the advanced few-shot learning methods are mainly divided into four categories based on: data augmentation, metric learning, external memory, and parameter optimization, solving the over-fitting problem from different viewpoints. This review comprehensively expounds on few-shot learning in smart agriculture, introduces the definition of few-shot learning, four kinds of learning methods, the publicly available datasets for few-shot learning, various applications in smart agriculture, and the challenges in smart agriculture in future development. 

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A survey of few-shot learning in smart agriculture: developments, applications, and challenges

With the rapid development of face manipulation technology, it is difficult for human eyes to distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) discriminators can quickly reach high accuracy in identifying fake/real face images. In this study, we explore the behavior of CNN models in distinguish fake/real faces. We find multi-scale texture difference information plays an important role in face forgery detection. Motivated by the above observation, we propose a new Multi-scale Texture Difference model coined as MTD-Net for robust face forgery detection, which leverages central difference convolution (CDC) and atrous spatial pyramid pooling (ASPP). CDC combines the pixel intensity information and the pixel gradient information to give a stationary description of texture difference information. Simultaneously, based on the ASPP, multi-scale information fusion can keep the texture features from being destroyed. Experimental results on several databases, Faceforensics++, DeeperForensics-1.0, Celeb-DF and DFDC prove that our MTD-Net outperforms existing approaches. The MTD-Net is more robust to image distortion, e.g. , JPEG compression and blur, which is urgently needed in the wild world.

MTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference

This paper proposes a commonality learning strategy for face video forgery detection to improve the generalization. Considering various face forgery methods could leave certain similar forgery traces in videos, we attempt to learn the common forgery features from different forgery databases, so as to achieve better generalization in the detection of unknown forgery methods. Firstly, the Specific Forgery Feature Extractors (SFFExtractors) are trained separately for each of given forgery methods. We utilize the U-net structure and consider the triplet loss, location loss, classification loss, and automatic weighted loss to ensure the detection ability of SFFExtractors on the corresponding forgery methods. Next, the Common Forgery Feature Extractor (CFFExtractor) is trained under the supervision of SFFExtractors to explore the commonality of the forgery traces caused by different forgery methods. The extracted common forgery feature is expected to have a good generalization. The experimental results on FaceForensic++ show that the SFFExtractors outperform many state-of-the-arts in face forgery detection. The generalization performance of the CFFExtractor is verified on FaceForensic++, DFDC, and CelebDF. It is proved that commonality learning can be an effective strategy to improve generalization.

Improving Generalization by Commonality Learning in Face Forgery Detection

Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, and open-source trained models, along with economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods have heralded a new and frightening trend. Particularly, the advent of easily available and ready to use Generative Adversarial Networks (GANs), have made it possible to generate deepfakes media partially or completely fabricated with the intent to deceive to disseminate disinformation and revenge porn, to perpetrate financial frauds and other hoaxes, and to disrupt government functioning. Existing surveys have mainly focused on the detection of deepfake images and videos; this paper provides a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation, and the methodologies used to detect such manipulations in both audio and video. For each category of deepfake, we discuss information related to manipulation approaches, current public datasets, and key standards for the evaluation of the performance of deepfake detection techniques, along with their results. Additionally, we also discuss open challenges and enumerate future directions to guide researchers on issues which need to be considered in order to improve the domains of both deepfake generation and detection. This work is expected to assist readers in understanding how deepfakes are created and detected, along with their current limitations and where future research may lead. 

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Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward

Lots of Deepfake videos are circulating on the Internet, which not only damages the personal rights of the forged individual, but also pollutes the web environment. What’s worse, it may trigger public opinion and endanger national security. Therefore, it is urgent to fight deep forgery. Most of the current forgery detection algorithms are based on convolutional neural networks to learn the feature differences between forged and real frames from big data. In this paper, from the perspective of image generation, we simulate the forgery process based on image generation and explore possible trace of forgery. We propose a multi-scale self-texture attention Generative Network(MSTA-Net) to track the potential texture trace in image generation process and eliminate the interference of deep forgery post-processing. Firstly, a generator with encoder-decoder is to disassemble images and performed trace generation, then we merge the generated trace image and the original map, which is input into the classifier with Resnet as the backbone. Secondly, the self-texture attention mechanism(STA) is proposed as the skip connection between the encoder and the decoder, which significantly enhances the texture characteristics in the image disassembly process and assists the generation of texture trace. Finally, we propose a loss function called Prob-tuple loss restricted by classification probability to amend the generation of forgery trace directly. To verify the performance of the MSTA-Net, we design different experiments to verify the feasibility and advancement of the method. Experimental results show that the proposed method performs well on deep forged databases represented by FaceForensics++, Celeb-DF, Deeperforensics and DFDC, and some results are reaching the state-of-the-art. 

Aiyun Li

Papers

MTD-Net: Learning to Detect Deepfakes Images by Multi-Scale Texture Difference

Detecting fake images by identifying potential texture difference