Incorporating human feedback to optimize text-to-image models has demonstrated significant effectiveness. However, the process of collecting high-quality human preference labels is both resource-intensive and time-consuming. To address this challenge, we propose a novel approach that leverages a large language model (LLM) to generate sophisticated prompts, guiding the diffusion model towards enhanced image generation. This process inherently produces ranking pairs that approximate human preferences. We further introduce a novel integration of AI feedback with a Supervised Fine-Tuning (SFT) policy, aligning the model with preference labels derived from AI. Our experiments demonstrate that our approach achieves a notable approximation of human preferences, achieving a performance level of 68.13% compared to human-level benchmarks and delivering competitive results. Furthermore, we showcase the synergistic effects of combining AI feedback with human feedback, resulting in further improvements in image quality. This research offers fresh insights into AI feedback learning within text-to-image generation and lays the groundwork for more efficient and cost-effective training methodologies.

Recently, there has been significant progress in leveraging human feedback to enhance diffusion-based image generation, garnering considerable interest and attention. However, existing methods fail to achieve a fine-grained performance boost for the following challenges: i) insufficient amount of fine-grained feedback data; ii) lack of effective fine-grained feedback learning framework; To tackle these challenges, we present TreeReward to facilitate the fine-grained feedback optimization for diffusion models. Specifically, to address the limitation of the fine-grained feedback data, we first design a novel "AI + Expert" feedback data construction pipeline, yielding about 2.2M high-quality feedback dataset encompassing six fine-grained dimensions at a relatively low cost. Built upon this dataset, we introduce a tree-structure reward model to exploit the fine-grained feedback data efficiently and provide tailored optimization during feedback learning. We validate the feedback learning performance of our method across different fine-grained dimensions and various downstream tasks. Extensive experiments on both Stable Diffusion v1.5 (SD1.5) and Stable Diffusion XL (SDXL) demonstrate the effectiveness of our method in enhancing the general and fine-grained generation and downstream tasks generalization.

The rapid progress in generative models has given rise to the critical task of AI-Generated Content Stealth (AIGC-S), which aims to create AI-generated images that can evade both forensic detectors and human inspection. This task is crucial for understanding the vulnerabilities of existing detection methods and developing more robust techniques. However, current adversarial attacks often introduce visible noise, have poor transferability, and fail to address spectral differences between AI-generated and genuine images. To address this, we propose StealthDiffusion, a framework based on stable diffusion that modifies AI-generated images into high-quality, imperceptible adversarial examples capable of evading state-of-the-art forensic detectors. StealthDiffusion comprises two main components: Latent Adversarial Optimization, which generates adversarial perturbations in the latent space of stable diffusion, and Control-VAE, a module that reduces spectral differences between the generated adversarial images and genuine images without affecting the original diffusion model's generation process. Extensive experiments show that StealthDiffusion is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries with frequency spectra similar to genuine images. These forgeries are classified as genuine by advanced forensic classifiers and are difficult for humans to distinguish.

Latent diffusion models (LDM) have revolutionized text-to-image generation, leading to the proliferation of various advanced models and diverse downstream applications. However, despite these significant advancements, current diffusion models still suffer from several limitations, including inferior visual quality, inadequate aesthetic appeal, and inefficient inference, without a comprehensive solution in sight. To address these challenges, we present **UniFL**, a unified framework that leverages feedback learning to enhance diffusion models comprehensively. UniFL stands out as a universal, effective, and generalizable solution applicable to various diffusion models, such as SD1.5 and SDXL. Notably, UniFL consists of three key components: perceptual feedback learning, which enhances visual quality; decoupled feedback learning, which improves aesthetic appeal; and adversarial feedback learning, which accelerates inference. In-depth experiments and extensive user studies validate the superior performance of our method in enhancing generation quality and inference acceleration. For instance, UniFL surpasses ImageReward by 17\% user preference in terms of generation quality and outperforms LCM and SDXL Turbo by 57\% and 20\% general preference with 4-step inference.

Recent works have indicated that deep neural networks (DNNs) are vulnerable to adversarial attacks, wherein an attacker perturbs an input example with human-imperceptible noise that can easily fool the DNNs, resulting in incorrect predictions. This severely limits the application of deep learning in security-critical scenarios, such as face authentication. Adversarial training (AT) is one of the most practical approaches to strengthening the robustness of DNNs. However, existing AT-based methods treat each training sample independently, thereby ignoring the underlying topological structure in the training data. To this end, in this paper, we take full advantage of the topology information and introduce a Topology Aligning Adversarial Training (TAAT) algorithm. TAAT aims to encourage the trained model to maintain consistency in the topological structure within the feature space of both natural and adversarial examples. To ensure the stability and efficiency of topology alignment, we further introduce a novel Knowledge-Guided (KG) training scheme. This scheme explicitly aligns local logit outputs with global topological structures, leveraging a robust auxiliary model to enhance the target model’s performance. To verify the effectiveness of the proposed method, we conduct extensive experiments on popular benchmark datasets (e.g., CIFAR and ImageNet) and evaluate the robustness against state-of-the-art adversarial attacks (e.g., PGD-attack and AutoAttack). The experimental results demonstrate that the proposed method has superior robustness over the previous state-of-the-art methods. Our code and pre-trained models are available at https://github.com/SkyKuang/TAAT.

Previous studies have shown that optimizing the information bottleneck can significantly improve the robustness of deep neural networks. Our study closely examines the information bottleneck principle and proposes an Information Bottleneck Distillation approach. This specially designed, robust distillation technique utilizes prior knowledge obtained from a robust pre-trained model to boost information bottlenecks. Specifically, we propose two distillation strategies that align with the two optimization processes of the information bottleneck. Firstly, we use a robust soft-label distillation method to increase the mutual information between latent features and output prediction. Secondly, we introduce an adaptive feature distillation method that automatically transfers relevant knowledge from the teacher model to the student model, thereby reducing the mutual information between the input and latent features. We conduct extensive experiments to evaluate our approach's robustness against state-of-the-art adversarial attackers such as PGD-attack and AutoAttack. Our experimental results demonstrate the effectiveness of our approach in significantly improving adversarial robustness. Our code is available at https://github.com/SkyKuang/IBD.

Deep neural networks have been widely used in various domains owing to the success of deep learning. However, recent studies have shown that these models are vulnerable to adversarial examples, leading to inaccurate predictions. In this paper, we focus on the issue of adversarial robustness by examining it through the lens of semantic information, which drives us to propose a new perspective,i.e., adversarial attacks destroy the correlation between visual representations and semantic word vectors, while adversarial training restores it. Additionally, we discover that the correlation among robust representations of different categories aligns with the correlation among the corresponding semantic word vectors. Based on these empirical observations, we incorporate the semantic information into the model training process and propose Semantic Constraint Adversarial Robust Learning (SCARL). Firstly, inspired by the information-theoretical perspective, we maximize mutual information to bridge the information gap between the visual representations and the corresponding semantic word vectors in the embedding space. We further provide a differentiable lower bound to optimize such mutual information efficiently. Secondly, we introduce a novel semantic structure constraint that maintains the structure of visual representations consistent with that of semantic word vectors. Finally, we integrate these techniques with adversarial training to learn robust visual representations. We conduct extensive experiments on several datasets (such as CIFAR and TinyImageNet) and evaluate the robustness against various adversarial attacks (such as PGD-attack and AutoAttack), demonstrating the benefits of incorporating semantic information for improving model robustness. Our code is available at https://github.com/SkyKuang/SCARL.

Face anti-spoofing detection is critical to guarantee the security of biometric face recognition systems. Despite extensive advances in facial anti-spoofing based on single-model image, little work has been devoted to multi-modal anti-spoofing, which is however widely encountered in real-world scenarios. Following the recent progress, this paper mainly focuses on multi-modal face anti-spoofing and aims to solve the following two challenges: (1) how to effectively fuse multi-modal information; and (2) how to effectively learn distinguishable features despite single cross-entropy loss. We propose a novel Multi-modal Multi-layer Fusion Convolutional Neural Network (mmfCNN), which targets at finding a discriminative model for recognizing the subtle differences between live and spoof faces. The mmfCNN can fully use different information provided by diverse modalities, which is based on a weight-adaptation aggregation approach. Specifically, we utilize a multi-layer fusion model to further aggregate the features from different layers, which fuses the low-, mid- and high-level information from different modalities in a unified framework. Moreover, a novel Average Binary Center (ABC) loss is proposed to maximize the dissimilarity between the features of live and spoof faces, which helps to stabilize the training to generate a robust and discriminative model. Extensive experiments conducted on the CISIA-SURF and 3DMAD datasets verify the significance and generalization capability of the proposed method for the face anti-spoofing task. Code is available at: https://github.com/SkyKuang/Face-anti-spoofing.