Image camouflage has been utilized to create clean-label poisoned images for implanting backdoor into a DL model. But there exists a crucial limitation that one attack/poisoned image can only fit a single input size of the DL model, which greatly increases its attack budget when attacking multiple commonly adopted input sizes of DL models. This work proposes to constructively craft an attack image through camouflaging but can fit multiple DL models' input sizes simultaneously, namely OmClic. Thus, through OmClic, we are able to always implant a backdoor regardless of which common input size is chosen by the user to train the DL model given the same attack budget (i.e., a fraction of the poisoning rate). With our camouflaging algorithm formulated as a multi-objective optimization, M=5 input sizes can be concurrently targeted with one attack image, which artifact is retained to be almost visually imperceptible at the same time. Extensive evaluations validate the proposed OmClic can reliably succeed in various settings using diverse types of images. Further experiments on OmClic based backdoor insertion to DL models show that high backdoor performances (i.e., attack success rate and clean data accuracy) are achievable no matter which common input size is randomly chosen by the user to train the model. So that the OmClic based backdoor attack budget is reduced by M$\times$ compared to the state-of-the-art camouflage based backdoor attack as a baseline. Significantly, the same set of OmClic based poisonous attack images is transferable to different model architectures for backdoor implant.

翻译：图像伪装已被用于生成清洁标签的毒化图像，以向深度学习模型植入后门。但现有方法存在关键局限：每张攻击/毒化图像仅能适配深度学习模型的单一输入尺寸，当需要攻击多种常用输入尺寸时，这会极大增加攻击成本。本文提出通过伪装技术构造性生成可同时适配多种深度学习模型输入尺寸的攻击图像（即OmClic方法）。因此，在相同攻击预算（即部分毒化率）下，无论用户选择何种常见输入尺寸训练模型，OmClic均可实现后门植入。通过将伪装算法建模为多目标优化问题，单张攻击图像可同时适配M=5种输入尺寸，同时保持伪影近乎视觉不可见。大量评估表明，OmClic在使用不同类别图像的各种设置中均能可靠成功。进一步基于OmClic的深度学习模型后门植入实验显示：无论用户随机选择哪种常见输入尺寸训练模型，均可获得高后门性能（即攻击成功率和干净数据准确率）。与现有基于伪装的先进后门攻击方法相比，OmClic后门攻击成本降低至1/M。值得注意的是，同一组OmClic毒化攻击图像可迁移至不同模型架构实现后门植入。