OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service

Local life service is a vital scenario in Kuaishou App, where video recommendation is intrinsically linked with store's location information. Thus, recommendation in our scenario is challenging because we should take into account user's interest and real-time location at the same time. In the face of such complex scenarios, end-to-end generative recommendation has emerged as a new paradigm, such as OneRec in the short video scenario, OneSug in the search scenario, and EGA in the advertising scenario. However, in local life service, an end-to-end generative recommendation model has not yet been developed as there are some key challenges to be solved. The first challenge is how to make full use of geographic information. The second challenge is how to balance multiple objectives, including user interests, the distance between user and stores, and some other business objectives. To address the challenges, we propose OneLoc. Specifically, we leverage geographic information from different perspectives: (1) geo-aware semantic ID incorporates both video and geographic information for tokenization, (2) geo-aware self-attention in the encoder leverages both video location similarity and user's real-time location, and (3) neighbor-aware prompt captures rich context information surrounding users for generation. To balance multiple objectives, we use reinforcement learning and propose two reward functions, i.e., geographic reward and GMV reward. With the above design, OneLoc achieves outstanding offline and online performance. In fact, OneLoc has been deployed in local life service of Kuaishou App. It serves 400 million active users daily, achieving 21.016% and 17.891% improvements in terms of gross merchandise value (GMV) and orders numbers.

Utilizing Jailbreak Probability to Attack and Safeguard Multimodal LLMs

Recently, Multimodal Large Language Models (MLLMs) have demonstrated their superior ability in understanding multimodal contents. However, they remain vulnerable to jailbreak attacks, which exploit weaknesses in their safety alignment to generate harmful responses. Previous studies categorize jailbreaks as successful or failed based on whether responses contain malicious content. However, given the stochastic nature of MLLM responses, this binary classification of an input's ability to jailbreak MLLMs is inappropriate. Derived from this viewpoint, we introduce jailbreak probability to quantify the jailbreak potential of an input, which represents the likelihood that MLLMs generated a malicious response when prompted with this input. We approximate this probability through multiple queries to MLLMs. After modeling the relationship between input hidden states and their corresponding jailbreak probability using Jailbreak Probability Prediction Network (JPPN), we use continuous jailbreak probability for optimization. Specifically, we propose Jailbreak-Probability-based Attack (JPA) that optimizes adversarial perturbations on inputs to maximize jailbreak probability. To counteract attacks, we also propose two defensive methods: Jailbreak-Probability-based Finetuning (JPF) and Jailbreak-Probability-based Defensive Noise (JPDN), which minimizes jailbreak probability in the MLLM parameters and input space, respectively. Extensive experiments show that (1) JPA yields improvements (up to 28.38\%) under both white and black box settings compared to previous methods with small perturbation bounds and few iterations. (2) JPF and JPDN significantly reduce jailbreaks by at most over 60\%. Both of the above results demonstrate the significance of introducing jailbreak probability to make nuanced distinctions among input jailbreak abilities.

DuMo: Dual Encoder Modulation Network for Precise Concept Erasure

The exceptional generative capability of text-to-image models has raised substantial safety concerns regarding the generation of Not-Safe-For-Work (NSFW) content and potential copyright infringement. To address these concerns, previous methods safeguard the models by eliminating inappropriate concepts. Nonetheless, these models alter the parameters of the backbone network and exert considerable influences on the structural (low-frequency) components of the image, which undermines the model's ability to retain non-target concepts. In this work, we propose our Dual encoder Modulation network (DuMo), which achieves precise erasure of inappropriate target concepts with minimum impairment to non-target concepts. In contrast to previous methods, DuMo employs the Eraser with PRior Knowledge (EPR) module which modifies the skip connection features of the U-NET and primarily achieves concept erasure on details (high-frequency) components of the image. To minimize the damage to non-target concepts during erasure, the parameters of the backbone U-NET are frozen and the prior knowledge from the original skip connection features is introduced to the erasure process. Meanwhile, the phenomenon is observed that distinct erasing preferences for the image structure and details are demonstrated by the EPR at different timesteps and layers. Therefore, we adopt a novel Time-Layer MOdulation process (TLMO) that adjusts the erasure scale of EPR module's outputs across different layers and timesteps, automatically balancing the erasure effects and model's generative ability. Our method achieves state-of-the-art performance on Explicit Content Erasure, Cartoon Concept Removal and Artistic Style Erasure, clearly outperforming alternative methods. Code is available at https://github.com/Maplebb/DuMo

Emoji Attack: A Method for Misleading Judge LLMs in Safety Risk Detection

Jailbreaking attacks show how Large Language Models (LLMs) can be tricked into generating harmful outputs using malicious prompts. To prevent these attacks, other LLMs are often used as judges to evaluate the harmfulness of the generated content. However, relying on LLMs as judges can introduce biases into the detection process, which in turn compromises the effectiveness of the evaluation. In this paper, we show that Judge LLMs, like other LLMs, are also affected by token segmentation bias. This bias occurs when tokens are split into smaller sub-tokens, altering their embeddings. This makes it harder for the model to detect harmful content. Specifically, this bias can cause sub-tokens to differ significantly from the original token in the embedding space, leading to incorrect "safe" predictions for harmful content. To exploit this bias in Judge LLMs, we introduce the Emoji Attack -- a method that places emojis within tokens to increase the embedding differences between sub-tokens and their originals. These emojis create new tokens that further distort the token embeddings, exacerbating the bias. To counter the Emoji Attack, we design prompts that help LLMs filter out unusual characters. However, this defense can still be bypassed by using a mix of emojis and other characters. The Emoji Attack can also be combined with existing jailbreaking prompts using few-shot learning, which enables LLMs to generate harmful responses with emojis. These responses are often mistakenly labeled as "safe" by Judge LLMs, allowing the attack to slip through. Our experiments with six state-of-the-art Judge LLMs show that the Emoji Attack allows 25\% of harmful responses to bypass detection by Llama Guard and Llama Guard 2, and up to 75\% by ShieldLM. These results highlight the need for stronger Judge LLMs to address this vulnerability.

ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack

Recent diffusion-based unrestricted attacks generate imperceptible adversarial examples with high transferability compared to previous unrestricted attacks and restricted attacks. However, existing works on diffusion-based unrestricted attacks are mostly focused on images yet are seldom explored in videos. In this paper, we propose the Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack (ReToMe-VA), which is the first framework to generate imperceptible adversarial video clips with higher transferability. Specifically, to achieve spatial imperceptibility, ReToMe-VA adopts a Timestep-wise Adversarial Latent Optimization (TALO) strategy that optimizes perturbations in diffusion models' latent space at each denoising step. TALO offers iterative and accurate updates to generate more powerful adversarial frames. TALO can further reduce memory consumption in gradient computation. Moreover, to achieve temporal imperceptibility, ReToMe-VA introduces a Recursive Token Merging (ReToMe) mechanism by matching and merging tokens across video frames in the self-attention module, resulting in temporally consistent adversarial videos. ReToMe concurrently facilitates inter-frame interactions into the attack process, inducing more diverse and robust gradients, thus leading to better adversarial transferability. Extensive experiments demonstrate the efficacy of ReToMe-VA, particularly in surpassing state-of-the-art attacks in adversarial transferability by more than 14.16% on average.

Reliable and Efficient Concept Erasure of Text-to-Image Diffusion Models

Text-to-image models encounter safety issues, including concerns related to copyright and Not-Safe-For-Work (NSFW) content. Despite several methods have been proposed for erasing inappropriate concepts from diffusion models, they often exhibit incomplete erasure, consume a lot of computing resources, and inadvertently damage generation ability. In this work, we introduce Reliable and Efficient Concept Erasure (RECE), a novel approach that modifies the model in 3 seconds without necessitating additional fine-tuning. Specifically, RECE efficiently leverages a closed-form solution to derive new target embeddings, which are capable of regenerating erased concepts within the unlearned model. To mitigate inappropriate content potentially represented by derived embeddings, RECE further aligns them with harmless concepts in cross-attention layers. The derivation and erasure of new representation embeddings are conducted iteratively to achieve a thorough erasure of inappropriate concepts. Besides, to preserve the model's generation ability, RECE introduces an additional regularization term during the derivation process, resulting in minimizing the impact on unrelated concepts during the erasure process. All the processes above are in closed-form, guaranteeing extremely efficient erasure in only 3 seconds. Benchmarking against previous approaches, our method achieves more efficient and thorough erasure with minor damage to original generation ability and demonstrates enhanced robustness against red-teaming tools. Code is available at \url{https://github.com/CharlesGong12/RECE}.

Incorporating Locality of Images to Generate Targeted Transferable Adversarial Examples

Despite that leveraging the transferability of adversarial examples can attain a fairly high attack success rate for non-targeted attacks, it does not work well in targeted attacks since the gradient directions from a source image to a targeted class are usually different in different DNNs. To increase the transferability of target attacks, recent studies make efforts in aligning the feature of the generated adversarial example with the feature distributions of the targeted class learned from an auxiliary network or a generative adversarial network. However, these works assume that the training dataset is available and require a lot of time to train networks, which makes it hard to apply to real-world scenarios. In this paper, we revisit adversarial examples with targeted transferability from the perspective of universality and find that highly universal adversarial perturbations tend to be more transferable. Based on this observation, we propose the Locality of Images (LI) attack to improve targeted transferability. Specifically, instead of using the classification loss only, LI introduces a feature similarity loss between intermediate features from adversarial perturbed original images and randomly cropped images, which makes the features from adversarial perturbations to be more dominant than that of benign images, hence improving targeted transferability. Through incorporating locality of images into optimizing perturbations, the LI attack emphasizes that targeted perturbations should be universal to diverse input patterns, even local image patches. Extensive experiments demonstrate that LI can achieve high success rates for transfer-based targeted attacks. On attacking the ImageNet-compatible dataset, LI yields an improvement of 12\% compared with existing state-of-the-art methods.

Cross-Modal Transferable Adversarial Attacks from Images to Videos

Recent studies have shown that adversarial examples hand-crafted on one white-box model can be used to attack other black-box models. Such cross-model transferability makes it feasible to perform black-box attacks, which has raised security concerns for real-world DNNs applications. Nevertheless, existing works mostly focus on investigating the adversarial transferability across different deep models that share the same modality of input data. The cross-modal transferability of adversarial perturbation has never been explored. This paper investigates the transferability of adversarial perturbation across different modalities, i.e., leveraging adversarial perturbation generated on white-box image models to attack black-box video models. Specifically, motivated by the observation that the low-level feature space between images and video frames are similar, we propose a simple yet effective cross-modal attack method, named as Image To Video (I2V) attack. I2V generates adversarial frames by minimizing the cosine similarity between features of pre-trained image models from adversarial and benign examples, then combines the generated adversarial frames to perform black-box attacks on video recognition models. Extensive experiments demonstrate that I2V can achieve high attack success rates on different black-box video recognition models. On Kinetics-400 and UCF-101, I2V achieves an average attack success rate of 77.88% and 65.68%, respectively, which sheds light on the feasibility of cross-modal adversarial attacks.

Attacking Video Recognition Models with Bullet-Screen Comments

Recent research has demonstrated that Deep Neural Networks (DNNs) are vulnerable to adversarial patches which introducing perceptible but localized changes to the input. Nevertheless, existing approaches have focused on generating adversarial patches on images, their counterparts in videos have been less explored. Compared with images, attacking videos is much more challenging as it needs to consider not only spatial cues but also temporal cues. To close this gap, we introduce a novel adversarial attack in this paper, the bullet-screen comment (BSC) attack, which attacks video recognition models with BSCs. Specifically, adversarial BSCs are generated with a Reinforcement Learning (RL) framework, where the environment is set as the target model and the agent plays the role of selecting the position and transparency of each BSC. By continuously querying the target models and receiving feedback, the agent gradually adjusts its selection strategies in order to achieve a high fooling rate with non-overlapping BSCs. As BSCs can be regarded as a kind of meaningful patch, adding it to a clean video will not affect people' s understanding of the video content, nor will arouse people' s suspicion. We conduct extensive experiments to verify the effectiveness of the proposed method. On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90\% fooling rate when attack three mainstream video recognition models, while only occluding \textless 8\% areas in the video.

Boosting the Transferability of Video Adversarial Examples via Temporal Translation

Although deep-learning based video recognition models have achieved remarkable success, they are vulnerable to adversarial examples that are generated by adding human-imperceptible perturbations on clean video samples. As indicated in recent studies, adversarial examples are transferable, which makes it feasible for black-box attacks in real-world applications. Nevertheless, most existing adversarial attack methods have poor transferability when attacking other video models and transfer-based attacks on video models are still unexplored. To this end, we propose to boost the transferability of video adversarial examples for black-box attacks on video recognition models. Through extensive analysis, we discover that different video recognition models rely on different discriminative temporal patterns, leading to the poor transferability of video adversarial examples. This motivates us to introduce a temporal translation attack method, which optimizes the adversarial perturbations over a set of temporal translated video clips. By generating adversarial examples over translated videos, the resulting adversarial examples are less sensitive to temporal patterns existed in the white-box model being attacked and thus can be better transferred. Extensive experiments on the Kinetics-400 dataset and the UCF-101 dataset demonstrate that our method can significantly boost the transferability of video adversarial examples. For transfer-based attack against video recognition models, it achieves a 61.56% average attack success rate on the Kinetics-400 and 48.60% on the UCF-101.