arXiv subjects

Qichao Ying

Publications and source records attributed to Qichao Ying.

26 records · Page 2Linked to original sources

arXiv2022

Robust Watermarking for Video Forgery Detection with Improved Imperceptibility and Robustness

Videos are prone to tampering attacks that alter the meaning and deceive the audience. Previous video forgery detection schemes find tiny clues to locate the tampered areas. However, attackers can successfully evade supervision by destroying such clues using video compression or blurring. This paper proposes a video watermarking network for tampering localization. We jointly train a 3D-UNet-based watermark embedding network and a decoder that predicts the tampering mask. The perturbation made by watermark embedding is close to imperceptible. Considering that there is no off-the-shelf differentiable video codec simulator, we propose to mimic video compression by ensembling simulation results of other typical attacks, e.g., JPEG compression and blurring, as an approximation. Experimental results demonstrate that our method generates watermarked videos with good imperceptibility and robustly and accurately locates tampered areas within the attacked version.

cs.CV↗

arXiv2022

A DTCWT-SVD Based Video Watermarking resistant to frame rate conversion

Videos can be easily tampered, copied and redistributed by attackers for illegal and monetary usage. Such behaviors severely jeopardize the interest of content owners. Despite huge efforts made in digital video watermarking for copyright protection, typical distortions in video transmission including signal attacks, geometric attacks and temporal synchronization attacks can still easily erase the embedded signal. Among them, temporal synchronization attacks which include frame dropping, frame insertion and frame rate conversion is one of the most prevalent attacks. To address this issue, we present a new video watermarking based on joint Dual-Tree Cosine Wavelet Transformation (DTCWT) and Singular Value Decomposition (SVD), which is resistant to frame rate conversion. We first extract a set of candidate coefficient by applying SVD decomposition after DTCWT transform. Then, we simulate the watermark embedding by adjusting the shape of candidate coefficient. Finally, we perform group-level watermarking that includes moderate temporal redundancy to resist temporal desynchronization attacks. Extensive experimental results show that the proposed scheme is more resilient to temporal desynchronization attacks and performs better than the existing blind video watermarking schemes.

cs.MM↗

arXiv2022

RWN: Robust Watermarking Network for Image Cropping Localization

Image cropping can be maliciously used to manipulate the layout of an image and alter the underlying meaning. Previous image crop detection schemes only predicts whether an image has been cropped, ignoring which part of the image is cropped. This paper presents a novel robust watermarking network (RWN) for image crop localization. We train an anti-crop processor (ACP) that embeds a watermark into a target image. The visually indistinguishable protected image is then posted on the social network instead of the original image. At the recipient's side, ACP extracts the watermark from the attacked image, and we conduct feature matching on the original and extracted watermark to locate the position of the crop in the original image plane. We further extend our scheme to detect tampering attack on the attacked image. Besides, we explore a simple yet efficient method (JPEG-Mixup) to improve the generalization of JPEG robustness. According to our comprehensive experiments, RWN is the first to provide high-accuracy and robust image crop localization. Besides, the accuracy of tamper detection is comparable with many state-of-the-art passive-based methods.

cs.CV↗

arXiv2022

Multimodal Fake News Detection via CLIP-Guided Learning

Multimodal fake news detection has attracted many research interests in social forensics. Many existing approaches introduce tailored attention mechanisms to guide the fusion of unimodal features. However, how the similarity of these features is calculated and how it will affect the decision-making process in FND are still open questions. Besides, the potential of pretrained multi-modal feature learning models in fake news detection has not been well exploited. This paper proposes a FND-CLIP framework, i.e., a multimodal Fake News Detection network based on Contrastive Language-Image Pretraining (CLIP). Given a targeted multimodal news, we extract the deep representations from the image and text using a ResNet-based encoder, a BERT-based encoder and two pair-wise CLIP encoders. The multimodal feature is a concatenation of the CLIP-generated features weighted by the standardized cross-modal similarity of the two modalities. The extracted features are further processed for redundancy reduction before feeding them into the final classifier. We introduce a modality-wise attention module to adaptively reweight and aggregate the features. We have conducted extensive experiments on typical fake news datasets. The results indicate that the proposed framework has a better capability in mining crucial features for fake news detection. The proposed FND-CLIP can achieve better performances than previous works, i.e., 0.7\%, 6.8\% and 1.3\% improvements in overall accuracy on Weibo, Politifact and Gossipcop, respectively. Besides, we justify that CLIP-based learning can allow better flexibility on multimodal feature selection.

cs.CV↗

arXiv2022

High-Capacity Framework for Reversible Data Hiding in Encrypted Image Using Pixel Predictions and Entropy Encoding

While the existing vacating room before encryption (VRBE) based schemes can achieve decent embedding rate, the payloads of the existing vacating room after encryption (VRAE) based schemes are relatively low. To address this issue, this paper proposes a generalized framework for high-capacity RDHEI for both VRBE and VRAE cases. First, an efficient embedding room generation algorithm (ERGA) is designed to produce large embedding room by using pixel prediction and entropy encoding. Then, we propose two RDHEI schemes, one for VRBE, another for VRAE. In the VRBE scenario, the image owner generates the embedding room with ERGA and encrypts the preprocessed image by using the stream cipher with two encryption keys. Then, the data hider locates the embedding room and embeds the encrypted additional data. In the VRAE scenario, the cover image is encrypted by an improved block modulation and permutation encryption algorithm, where the spatial redundancy in the plain-text image is largely preserved. Then, the data hider applies ERGA on the encrypted image to generate the embedding room and conducts data embedding. For both schemes, the receivers with different authentication keys can respectively conduct error-free data extraction and/or error-free image recovery. The experimental results show that the two proposed schemes outperform many state-of-the-art RDHEI arts. Besides, the schemes can ensure high security level, where the original image can be hardly discovered from the encrypted version before and after data hiding by the unauthorized user.

cs.MM↗

arXiv2021

Invertible Image Dataset Protection

Deep learning has achieved enormous success in various industrial applications. Companies do not want their valuable data to be stolen by malicious employees to train pirated models. Nor do they wish the data analyzed by the competitors after using them online. We propose a novel solution for dataset protection in this scenario by robustly and reversibly transform the images into adversarial images. We develop a reversible adversarial example generator (RAEG) that introduces slight changes to the images to fool traditional classification models. Even though malicious attacks train pirated models based on the defensed versions of the protected images, RAEG can significantly weaken the functionality of these models. Meanwhile, the reversibility of RAEG ensures the performance of authorized models. Extensive experiments demonstrate that RAEG can better protect the data with slight distortion against adversarial defense than previous methods.

cs.CV↗

arXiv2021

From Image to Imuge: Immunized Image Generation

We introduce Imuge, an image tamper resilient generative scheme for image self-recovery. The traditional manner of concealing image content within the image are inflexible and fragile to diverse digital attack, i.e. image cropping and JPEG compression. To address this issue, we jointly train a U-Net backboned encoder, a tamper localization network and a decoder for image recovery. Given an original image, the encoder produces a visually indistinguishable immunized image. At the recipient's side, the verifying network localizes the malicious modifications, and the original content can be approximately recovered by the decoder, despite the presence of the attacks. Several strategies are proposed to boost the training efficiency. We demonstrate that our method can recover the details of the tampered regions with a high quality despite the presence of various kinds of attacks. Comprehensive ablation studies are conducted to validate our network designs.

cs.CV↗

arXiv2021

Hiding Images into Images with Real-world Robustness

The existing image embedding networks are basically vulnerable to malicious attacks such as JPEG compression and noise adding, not applicable for real-world copyright protection tasks. To solve this problem, we introduce a generative deep network based method for hiding images into images while assuring high-quality extraction from the destructive synthesized images. An embedding network is sequentially concatenated with an attack layer, a decoupling network and an image extraction network. The addition of decoupling network learns to extract the embedded watermark from the attacked image. We also pinpoint the weaknesses of the adversarial training for robustness in previous works and build our improved real-world attack simulator. Experimental results demonstrate the superiority of the proposed method against typical digital attacks by a large margin, as well as the performance boost of the recovered images with the aid of progressive recovery strategy. Besides, we are the first to robustly hide three secret images.

cs.CV↗