arXiv subjects

Jinseo Kim

Publications and source records attributed to Jinseo Kim.

3 recordsLinked to original sources

arXiv2026

Decoupled Guidance: Disentangling Subject and Context Pathways in Text-to-Image Personalization

Text-to-image personalization aims to generate a user-provided subject in novel scenes described by text. However, most existing methods encode subject identity (fidelity) and context (editability) through the same conditioning pathway, forcing the two to compete for attention-map resources. We refer to this phenomenon as conditioning entanglement and show that it induces a fidelity-editability trade-off. We further provide causal evidence by replacing the target subject token with a generic subject token, which produces shifts in attention allocation and corresponding changes in context adherence. To this end, we propose Decoupled Guidance (DeGu), a plug-and-play framework that routes subject identity and scene context through two independent guidance streams. We further introduce a spatial mixing mechanism that dynamically fuses these streams, ensuring each operates within its semantically relevant region without interference. Furthermore, DeGu can be readily applied to existing personalization methods without modifying the underlying backbone models, consistently improving the overall personalization performance while enabling inference-time control over the fidelity-editability balance, across diverse methods and backbones, including flow-matching Diffusion Transformers (DiTs).

cs.CV↗

arXiv2026

Training-Free Debiasing of Diffusion Models via CLIP-Guided Denoising Optimization

Text-to-image diffusion models achieve impressive visual quality, yet demographic bias remains a challenge, as neutral prompts consistently produce stereotypical representations across gender and race. Existing approaches remain limited by costly retraining or by inference-time interventions that often degrade image quality and semantic alignment. We propose Text Embedding Steering (TES), a training-free framework that mitigates demographic bias by directly optimizing conditional text embeddings during the diffusion process. We show that a two-stage strategy - early-stage global alignment followed by iterative denoising-time refinement with CLIP-based feedback - enables stable and controllable attribute steering without modifying model parameters. Extensive experiments on Stable Diffusion demonstrate that TES outperforms existing training-free baselines in fairness while maintaining competitive image quality. These results highlight that inference-time text embedding optimization is a practical and scalable solution for fairness-aware generation in diffusion models.

cs.CV↗

arXiv2024

Automated Attack Synthesis for Constant Product Market Makers

Decentralized Finance (DeFi) enables many novel applications that were impossible in traditional finances. However, it also introduces new types of vulnerabilities. An example of such vulnerabilities is a composability bug between token contracts and Decentralized Exchange (DEX) that follows the Constant Product Market Maker (CPMM) model. This type of bug, which we refer to as CPMM composability bug, originates from issues in token contracts that make them incompatible with CPMMs, thereby endangering other tokens within the CPMM ecosystem. Since 2022, 23 exploits of such kind have resulted in a total loss of 2.2M USD. BlockSec, a smart contract auditing company, reported that 138 exploits of such kind occurred just in February 2023. In this paper, we propose CPMMX , a tool that automatically detects CPMM composability bugs across entire blockchains. To achieve such scalability, we first formalized CPMM composability bugs and found that these bugs can be induced by breaking two safety invariants. Based on this finding, we designed CPMMX equipped with a two-step approach, called shallow-then-deep search. In more detail, it first uses shallow search to find transactions that break the invariants. Then, it uses deep search to refine these transactions, making them profitable for the attacker. We evaluated CPMMX against five baselines on two public datasets and one synthetic dataset. In our evaluation, CPMMX detected 2.5x to 1.5x more vulnerabilities compared to baseline methods. It also analyzed contracts significantly faster, achieving higher F1 scores than the baselines. Additionally, we applied CPMMX to all contracts on the latest blocks of the Ethereum and Binance networks and discovered 26 new exploits that can result in 15.7K USD profit in total.

cs.CR↗