Identity-preserving models have led to notable progress in generating personalized content. Unfortunately, such models also exacerbate risks when misused, for instance, by generating threatening content targeting specific individuals. This paper introduces the Attribute Misbinding Attack, a novel method that poses a threat to identity-preserving models by inducing them to produce Not-Safe-For-Work (NSFW) content. The attack's core idea involves crafting benign-looking textual prompts to circumvent text-filter safeguards and leverage a key model vulnerability: flawed attribute binding that stems from its internal attention bias. This results in misattributing harmful descriptions to a target identity and generating NSFW outputs. To facilitate the study of this attack, we present the Misbinding Prompt evaluation set, which examines the content generation risks of current state-of-the-art identity-preserving models across four risk dimensions: pornography, violence, discrimination, and illegality. Additionally, we introduce the Attribute Binding Safety Score (ABSS), a metric for concurrently assessing both content fidelity and safety compliance. Experimental results show that our Misbinding Prompt evaluation set achieves a 5.28 % higher success rate in bypassing five leading text filters (including GPT-4o) compared to existing main-stream evaluation sets, while also demonstrating the highest proportion of NSFW content generation. The proposed ABSS metric enables a more comprehensive evaluation of identity-preserving models by concurrently assessing both content fidelity and safety compliance.

With the spread of tampered images, locating the tampered regions in digital images has drawn increasing attention. The existing tampering localization methods, however, suffer from severe performance degradation when the images are subjected to some post-processing, as the tampering traces would be distorted by the post-processing operations. The poor robustness against post-processing has become a bottleneck for the practical applications of image tampering localization techniques. In order to address this issue, this paper proposes a novelrestoration-assisted framework for image tamperinglocalization (ReLoc). The ReLoc framework mainly consists of an image restoration module and a tampering localization module. The key idea of ReLoc is to use the restoration module to recover a high-quality counterpart from the distorted tampered image, such that the distorted tampering traces can be re-enhanced, facilitating the tampering localization module to identify the tampered regions. To achieve this, the restoration module is optimized not only with the conventional constraints on image visual quality, but also with a forensics-oriented objective function. Furthermore, the restoration module and the localization module are trained alternately, which can stabilize the training process and is beneficial for improving the performance. The robustness of ReLoc has been evaluated by using several common post-processing operations, including lossy compressions, online social network transmission, and image resizing. Extensive experimental results show that ReLoc can significantly improve the localization performance compared to using a restoration-free model. In addition, we have shown that the restoration module in a well-trained ReLoc model is transferable for different localization modules and across different datasets.

The emergence of powerful image editing software has substantially facilitated digital image tampering, leading to many security issues. Hence, it is urgent to identify tampered images and localize tampered regions. Although much attention has been devoted to image tampering localization in recent years, it is still challenging to perform tampering localization in practical forensic applications. The reasons include the difficulty of learning discriminative representations of tampering traces and the lack of realistic tampered images for training. Since Photoshop is widely used for image tampering in practice, this paper attempts to address the issue of tampering localization by focusing on the detection of commonly used editing tools and operations in Photoshop. In order to well capture tampering traces, a fully convolutional encoder-decoder architecture is designed, where dense connections and dilated convolutions are adopted for achieving better localization performance. In order to effectively train a model in the case of insufficient tampered images, we design a training data generation strategy by resorting to Photoshop scripting, which can imitate human manipulations and generate large-scale training samples. Extensive experimental results show that the proposed approach outperforms state-of-the-art competitors when the model is trained with only generated images or fine-tuned with a small amount of realistic tampered images. The proposed method also has good robustness against some common post-processing operations.

The performance of modern Big Data frameworks, e.g. Spark, depends greatly on high-speed storage and shuffling, which impose a significant memory burden on production data centers. In many production situations, the persistence and shuffling intensive applications can suffer a major performance loss due to lack of memory. Thus, the common practice is usually to over-allocate the memory assigned to the data workers for production applications, which in turn reduces overall resource utilization. One efficient way to address the dilemma between the performance and cost efficiency of Big Data applications is through data center computing resource disaggregation. This paper proposes and implements a system that incorporates the Spark Big Data framework with a novel in-memory distributed file system to achieve memory disaggregation for data persistence and shuffling. We address the challenge of optimizing performance at affordable cost by co-designing the proposed in-memory distributed file system with large-volume DIMM-based persistent memory (PMEM) and RDMA technology. The disaggregation design allows each part of the system to be scaled independently, which is particularly suitable for cloud deployments. The proposed system is evaluated in a production-level cluster using real enterprise-level Spark production applications. The results of an empirical evaluation show that the system can achieve up to a 3.5- fold performance improvement for shuffle-intensive applications with the same amount of memory, compared to the default Spark setup. Moreover, by leveraging PMEM, we demonstrate that our system can effectively increase the memory capacity of the computing cluster with affordable cost, with a reasonable execution time overhead with respect to using local DRAM only.