eer.py

import numpy as np

def compute_det_curve(target_scores, nontarget_scores):
    n_scores = target_scores.size + nontarget_scores.size
    all_scores = np.concatenate((target_scores, nontarget_scores))
    labels = np.concatenate((np.ones(target_scores.size), np.zeros(nontarget_scores.size)))

    # Sort labels based on scores
    indices = np.argsort(all_scores, kind='mergesort')
    labels = labels[indices]

    # Compute false rejection and false acceptance rates
    tar_trial_sums = np.cumsum(labels)
    nontarget_trial_sums = nontarget_scores.size - (np.arange(1, n_scores + 1) - tar_trial_sums)

    frr = np.concatenate((np.atleast_1d(0), tar_trial_sums / target_scores.size))  # false rejection rates
    far = np.concatenate((np.atleast_1d(1), nontarget_trial_sums / nontarget_scores.size))  # false acceptance rates
    thresholds = np.concatenate((np.atleast_1d(all_scores[indices[0]] - 0.001), all_scores[indices]))  # Thresholds are the sorted scores

    return frr, far, thresholds

def compute_eer(ground_truth, predictions):
    """
    Expecting ground_truth and predictions to be numpy arrays of the same length;
    Defining deepfakes (ground_truth == 1) as target scores and bonafide (ground_truth == 0) as nontarget scores.
    """
    assert ground_truth.shape == predictions.shape, "ground_truth and predictions must have the same shape"
    assert len(ground_truth.shape) == 1, "ground_truth and predictions must be 1D arrays"
    
    target_scores = predictions[ground_truth == 1]
    nontarget_scores = predictions[ground_truth == 0]
    
    frr, far, thresholds = compute_det_curve(target_scores, nontarget_scores)
    abs_diffs = np.abs(frr - far)
    min_index = np.argmin(abs_diffs)
    eer = np.mean((frr[min_index], far[min_index]))
    return eer, thresholds[min_index]