Cross-table Synthetic Tabular Data Detection (2025)

Abstract: Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores whether synthetic tabular data can be reliably identified “in the wild”—meaning across different generators, domains, and table formats. This challenge is unique to tabular data, where structures (such as number of columns, data types, and formats) can vary widely from one table to another. We propose three cross-table baseline detectors and four distinct evaluation protocols, each corresponding to a different level of “wildness”. Our very preliminary results confirm that cross-table adaptation is a challenging task.

## introduction and related works most studies on synthetic data detection focus on image @xcite @xcite ), text @xcite @xcite @xcite , audio @xcite , video (face-swap) @xcite , or their combination @xcite . nevertheless, a growing number of generative models for tabular data generation has emerged recently; some are general-purpose @xcite , while others are tailored to specific domains like finance @xcite or healthcare @xcite . with these advances it will be easier to generate realistically manipulated datasets to fake scientific results or to hide fraud and accouting loopholes. it is therefore essential to focus research efforts on the detection of synthetic tabular data, and to develop detection techniques that are on par with the impressive generative models' capabilities. detecting syntetic content issued from a known generative model on a restricted domain is a fairly tractable task. the performance of such a predictor is indeed commonly used for adversarial training @xcite and as a metric to assess generation performance @xcite . however, the challenge intensifies when attempting to detect synthetic data "in the wild" @xcite , namely, when the deployed system has to face modalities and content generators it has never seen during its training phase. is is known that, even for homogeneous formats like image or text, synthetic content detection systems are not robust to such cross-generator and cross-domain distribution shifts @xcite . when dealing with tabular data, we have to face a stronger form of domain-shift that we call crosstable shift. indeed, for a synthetic table detection system to be useful, is has to cope with different table formats with varying numbers of columns, varying types and varying distributions shapes. although, the litterature on domain adaptation across the same table structure is vast (see @xcite , for a survey), only a few recent articles propose classifiers that are able to generalize across different tables @xcite . to the best of our knowledge, no study on cross-table synthetic data detection has been published yet. we present a preliminary work with three baselines for synthetic tabular data detection "in the wild." we focus on cross- synthetic data: our data generators are heavily tuned versions of tabddpm @xcite , tabsyn @xcite , tvae, and ctgan @xcite provided by @xcite . we trained the models on the entire real datasets before sampling new synthetic rows. each model is used to create a synthetic version of each dataset. ## detection models in order to be useful "in the wild", a detection model must be "table-agnostic", which means that it must accept inputs form different table formats. we trained three baselines for synthetic content detection from scratch: a logistic regression and two transformer-based classifiers. for the logistic regression and the first transformer the table is first linearized into text (section 3.1). for the second transformer-based classifier we use a rough columns level encoding of tables (section 3.2). the transformer-based classifiers have three main components: (i) a feature embedding block, (ii) a transformer encoder block, and (iii) a classification head. as in bert, the classifier relies on a cls embedding that is added to the input and retrieved in the output of the transformer blocks. the cls representation is fed to the classification head to predict the binary target class (real or synthetic data). the models (both text-based and tablebased) are trained using a binary cross entropy loss. ## experimental setup all dataset rows are mixed together in a list with two additional labels: the dataset name and the origin that can be "real" or the name of its generator if the row is synthetic. we use these two additonal labels to design cross-validation splits with increasingly challenging constraints: generator: table: single multiple, cross-table for instance, the classifier two-samples test (c2st) metric as described in (lopez-paz and oquab, 2016; zein and urvoy, 2022) correspond to the simplest single generator vs real, single table setting. it does not require a "table-agnostic" model. the cross-generator shift constraint guarantees that a generator used for trainning cannot be used in test. the cross-table constraint guarantees that a table used for trainning cannot be used in test. these single-criterion shift settings can be coded using scikit-learn groupkfold. however, as shown in ## results in this section, we present our baselines' results on different setups, without and with cross-table shift. these results are summarized in note that the only difference between the two approaches lies in the preprocessing and the way the feature embedding module works (as detailed in sections 3.2 and 3.1). this suggests that the textual representation offers a more general view across all models and datasets. as a side result, we notice that there is still significant room for improvement in achieving realism in tabular data generation. the synthetic tabular data generators seems to exhibit patterns that a naive table-agnostic classifier is able to detect. ## conclusion we study synthetic tabular data detection "in the wild". we utilized 14 datasets and 4 state-of-the-art, highly-tuned tabular data generation models. we evaluated various models using different tabular data representations as inputs and demonstrated that it is possible to detect synthetic data with promising performance. we also introduced various levels of "wildness" that correspond to different degrees of data distribution shift and we focused on cross-table shift. our preliminary results are encouraging but show that cross-table adaptation is still a challenging problem. in the future, we will consolidate these results and explore more sophisticated encodings and adaptation strategies such as including table metadata-like column names-in the input. we also plan to explore the adaptation of pretrained encoders like tabert to see if they reach the performance of our baselines on fake content detection.