Human faces contain rich semantic information that could hardly be described without a large vocabulary and complex sentence patterns. However, most existing text-to-image synthesis methods could only generate meaningful results based on limited sentence templates with words contained in the training set, which heavily impairs the generalization ability of these models. In this paper, we define a novel 'free-style' text-to-face generation and manipulation problem, and propose an effective solution, named AnyFace++, which is applicable to a much wider range of open-world scenarios. The CLIP model is involved in AnyFace++ for learning an aligned language-vision feature space, which also expands the range of acceptable vocabulary as it is trained on a large-scale dataset. To further improve the granularity of semantic alignment between text and images, a memory module is incorporated to convert the description with arbitrary length, format, and modality into regularized latent embeddings representing discriminative attributes of the target face. Moreover, the diversity and semantic consistency of generation results are improved by a novel semi-supervised training scheme and a series of newly proposed objective functions. Compared to state-of-the-art methods, AnyFace++ is capable of synthesizing and manipulating face images based on more flexible descriptions and producing realistic images with higher diversity.

Safeguarding data from unauthorized exploitation is vital for privacy and security, especially in recent rampant research in security breach such as adversarial/membership attacks. To this end,unlearnable examples (UEs) have been recently proposed as a compelling protection, by adding imperceptible perturbation to data so that models trained on them cannot classify them accurately on original clean distribution. Unfortunately, we find UEs provide a false sense of security, because they cannot stop unauthorized users from utilizing other unprotected data to remove the protection, by turning unlearnable data into learnable again. Motivated by this observation, we formally define a new threat by introducinglearnable unauthorized examples (LEs) which are UEs with their protection removed. The core of this approach is a novel purification process that projects UEs onto the manifold of LEs. This is realized by a new joint-conditional diffusion model which denoises UEs conditioned on the pixel and perceptual similarity between UEs and LEs. Extensive experiments demonstrate that LE delivers state-of-the-art countering performance against both supervised UEs and unsupervised UEs in various scenarios, which is the first generalizable countermeasure to UEs across supervised learning and unsupervised learning. Our code is available at https://github.com/jiangw-0/LE_JCDP.

Existing text-to-image synthesis methods generally are only applicable to words in the training dataset. However, human faces are so variable to be described with limited words. So this paper proposes the first free-style text-to-face method namely AnyFace enabling much wider open world applications such as metaverse, social media, cosmetics, forensics, etc. AnyFace has a novel two-stream framework for face image synthesis and manipulation given arbitrary descriptions of the human face. Specifically, one stream performs text-to-face generation and the other conducts face image reconstruction. Facial text and image features are extracted using the CLIP (Contrastive Language-Image Pre-training) encoders. And a collaborative Cross Modal Distillation (CMD) module is designed to align the linguistic and visual features across these two streams. Furthermore, a Diverse Triplet Loss (DT loss) is developed to model fine-grained features and improve facial diversity. Extensive experiments on Multi-modal CelebA-HQ and CelebAText-HQ demonstrate significant advantages of AnyFace over state-of-the-art methods. AnyFace can achieve high-quality, high-resolution, and high-diversity face synthesis and manipulation results without any constraints on the number and content of input captions.

Deep forgery detection on video data has attracted remarkable research attention in recent years due to its potential in defending forgery attacks. However, existing methods either only focus on the visual evidence within individual images, or are too sensitive to fluctuations across frames. To address these issues, this paper propose a novel model, named Bita-Net, to detect forgery faces in video data. The network design of Bita-Net is inspired by the mechanism of how human beings detect forgery data, i.e. browsing and scrutinizing, which is reflected by the two-pathway architecture of Bita-Net. Concretely, the browsing pathway scans the entire video at a high frame rate to check the temporal consistency, while the scrutinizing pathway focuses on analyzing key frames of the video at a lower frame rate. Furthermore, an attention branch is introduced to improve the forgery detection ability of the scrutinizing pathway. Extensive experiment results demonstrate the effectiveness and generalization ability of Bita-Net on various popular face forensics detection datasets, including FaceForensics++, CelebDF, DeepfakeTIMIT and UADFV.

Text-to-Face synthesis with multiple captions is still an important yet less addressed problem because of the lack of effective algorithms and large-scale datasets. We accordingly propose a Semantic Embedding and Attention (SEA-T2F) network that allows multiple captions as input to generate highly semantically related face images. With a novel Sentence Features Injection Module, SEA-T2F can integrate any number of captions into the network. In addition, an attention mechanism named Attention for Multiple Captions is proposed to fuse multiple word features and synthesize fine-grained details. Considering text-to-face generation is an ill-posed problem, we also introduce an attribute loss to guide the network to generate sentence-related attributes. Existing datasets for text-to-face are either too small or roughly generated according to attribute labels, which is not enough to train deep learning based methods to synthesize natural face images. Therefore, we build a large-scale dataset named CelebAText-HQ, in which each image is manually annotated with 10 captions. Extensive experiments demonstrate the effectiveness of our algorithm.